Improving prediction of water quality indices using novel hybrid machine-learning algorithms

Incorporation of water quality index models with machine learning-based techniques for real-time assessment of aquatic ecosystems

Water quality index (WQI) is a well-established tool for assessing the overall quality of fresh inland-waters. However, the effectiveness of real-time assessment of aquatic ecosystems using the WQI is usually impacted by the absence of some water quality parameters in which their accurately in-situ measurements are impossible and face difficulties. Using a rich water quality dataset spanned from 1980 to 2023, we employed four machine learning-based models to estimate the British Colombia WQI (BCWQI) in the Lake Päijänne, Finland, without parameters like chemical oxygen demand (COD) and total phosphorus (TP). Measurement of both COD and TP is time-consuming, needs laboratory equipment and labor costs, and faces sampling-related difficulties. Our results suggest the machine learning-based models successfully estimate the BCWQI in Lake Päijänne when TP and COD are omitted from the dataset. The long-short term memory model is the least sensitive model to exclusion of COD and TP from inputs. This model with the coefficient of determination and root-mean squared error of 0.91 and 0.11, respectively, outperforms the support vector regression, random forest, and neural network models in real-time estimation of the BCWQI in Lake Päijänne. Incorporation of BCWQI with the machine learning-based models could enhance assessment of overall quality of inland-waters with a limited database in a more economical and time-saving way. Our proposed method is an effort to replace the traditional offline water quality assessment tools with a real-time model and improve understanding of decision-makers on the effectiveness of management practices on the changes in lake water quality.

A critical analysis of parameter choices in water quality assessment

The determination of water quality heavily depends on the selection of parameters recorded from water samples for the water quality index (WQI). Data-driven methods, including machine learning models and statistical approaches, are frequently used to refine the parameter set for four main reasons: reducing cost and uncertainty, addressing the eclipsing problem, and enhancing the performance of models predicting the WQI. Despite their widespread use, there is a noticeable gap in comprehensive reviews that systematically examine previous studies in this area. Such reviews are essential to assess the validity of these objectives and to demonstrate the effectiveness of data-driven methods in achieving these goals. This paper sets out with two primary aims: first, to provide a review of the existing literature on methods for selecting parameters. Second, it seeks to delineate and evaluate the four principal motivations for parameter selection identified in the literature. This manuscript categorizes existing studies into two methodological groups for refining parameters: one focuses on preserving information within the dataset, and another ensures consistent prediction using the full set of parameters. It characterizes each group and evaluates how effectively each approach meets the four predefined objectives. The study presents that the minimal WQI approach, common to both categories, is the only approach that has successfully reduced recording costs. Nonetheless, it notes that simply reducing the number of parameters does not guarantee cost savings. Furthermore, the group of studies classified as preserving information within the dataset has demonstrated potential to decrease the eclipsing problem, whereas studies in the consistent prediction group have not been able to mitigate this issue. Additionally, since data-driven approaches still rely on the initial parameters chosen by experts, they do not eliminate the need for expert judgment. The study further points out that the WQI formula is a straightforward and expedient tool for assessing water quality. Consequently, the paper argues that employing machine learning solely to reduce the number of parameters to enhance WQI prediction is not a standalone solution. Rather, this objective should be integrated with a more comprehensive set of research goals. The critical analysis of research objectives and the characterization of previous studies lay the groundwork for future research. This groundwork will enable subsequent studies to evaluate how their proposed methods can effectively achieve these objectives.

Innovative interpretable AI-guided water quality evaluation with risk adversarial analysis in river streams considering spatial-temporal effects

Water security remains a critical issue given the looming threats of industrial pollution, necessitating comprehensive assessments of water quality to address seasonal fluctuations and influential factors while formulating effective strategies for decision makers. This study introduces a novel approach for evaluating water quality within a complex riverine zone in South Korea: Han River that encompasses five river streams situated at each junction of North and South streams (including Gyeongan Stream) that ultimately leading towards Paldang Lake. By utilizing the monthly water characteristic data from the year 2013-2022 across 14 different locations, the significant seasonal trends and potential influences on water quality are identified. The water quality here is calculated with the proposed method of sub-index water quality index (s-WQI). A combinatorial prediction approach of s-WQI for each location is conducted through a collective of data preprocessing approaches including Hampel filtering and feature selection in prior to the machine learning predictions. In return, light gradient boosting (LGB) is the most accurate predictor by outperforming other prediction algorithms, especially through LGB-Pearson and LGB-Spearman combinations for North and South stream intersections, and LGB-Pearson for Paldang Lake. To further evaluate the robustness of this evaluation and extending the results to a foreseeable scenario, a seasonal based Monte-Carlo Simulation with 10,000 attempts targeting the water characteristic distributions obtained from each location considered are carried out to identify the risk bounds within. The results are further interpreted with SHAP analysis on identifying the contributions of each water characteristics towards the water quality through local and global spectrum. This research yields practical implications, offering tailored strategies for water quality enhancement and early warning systems. The integration of AI-based prediction and feature selection underscores the transformative potential of computational techniques in advancing data-driven water quality assessments, shaping the future of environmental science research.

Ensemble machine learning using hydrometeorological information to improve modeling of quality parameter of raw water supplying treatment plants

Source and raw water quality may deteriorate due to rainfall and river flow events that occur in watersheds. The effects on raw water quality are normally detected in drinking water treatment plants (DWTPs) with a time-lag after these events in the watersheds. Early warning systems (EWSs) in DWTPs require models with high accuracy in order to anticipate changes in raw water quality parameters. Ensemble machine learning (EML) techniques have recently been used for water quality modeling to improve accuracy and decrease variance in the outcomes. We used three decision-tree-based EML models (random forest [RF], gradient boosting [GB], and eXtreme Gradient Boosting [XGB]) to predict two critical parameters for DWTPs, raw water Turbidity and UV absorbance (UV254), using rainfall and river flow time series as predictors. When modeling raw water turbidity, the three EML models (rRF-Tu2=0.87, rGB-Tu2=0.80 and rXGB-Tu2=0.81) showed very good performance metrics. For raw water UV254, the three models (rRF-UV2=0.89, rGB-UV2=0.85 and rXGB-UV2=0.88) again showed very good performance metrics. Results from this study suggest that EML approaches could be used in EWSs to anticipate changes in the quality parameters of raw water and enhance decision-making in DWTPs.

Stacked hybridization to enhance the performance of artificial neural networks (ANN) for prediction of water quality index in the Bagh river basin, India

Water quality assessment is paramount for environmental monitoring and resource management, particularly in regions experiencing rapid urbanization and industrialization. This study introduces Artificial Neural Networks (ANN) and its hybrid machine learning models, namely ANN-RF (Random Forest), ANN-SVM (Support Vector Machine), ANN-RSS (Random Subspace), ANN-M5P (M5 Pruned), and ANN-AR (Additive Regression) for water quality assessment in the rapidly urbanizing and industrializing Bagh River Basin, India. The Relief algorithm was employed to select the most influential water quality input parameters, including Nitrate (NO3-), Magnesium (Mg2+), Sulphate (SO42-), Calcium (Ca2+), and Potassium (K+). The comparative analysis of developed ANN and its hybrid models was carried out using statistical indicators (i.e., Nash-Sutcliffe Efficiency (NSE), Pearson Correlation Coefficient (PCC), Coefficient of Determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Root Square Error (RRSE), Relative Absolute Error (RAE), and Mean Bias Error (MBE)) and graphical representations (i.e., Taylor diagram). Results indicate that the integration of support vector machine (SVM) with ANN significantly improves performance, yielding impressive statistical indicators: NSE (0.879), R2 (0.904), MAE (22.349), and MBE (12.548). The methodology outlined in this study can serve as a template for enhancing the predictive capabilities of ANN models in various other environmental and ecological applications, contributing to sustainable development and safeguarding natural resources.

Estimating turbidity concentrations in highly dynamic rivers using Sentinel-2 imagery in Google Earth Engine: Case study of the Godavari River, India

Turbidity is an essential biogeochemical parameter for water quality management because it shapes the physical landscape and regulates ecological systems. It varies spatially and temporally across large water bodies, but monitoring based on point-source field observations remains a difficult task in developing countries due to the need for logistics and costs. In this study, we present a novel semi-analytical approach for estimating turbidity from remote sensing reflectance ( R rs ) in moderate to highly turbid waters in the lower part of the Godavari River (i.e., locations near Rajahmundry). The proposed method includes two sub-algorithms-Normalized Difference Turbidity Index (NDTI) and semi-empirical single-band turbidity ( T s ) algorithm-to retrieve spectral reflectance information corresponding to the study locations for turbidity modeling. Sentinel-2 Multi-Spectral Imager data have been used to quantify the turbidity in the Google Earth Engine (GEE) platform. The correlation analysis was observed between spectral reflectance values and in situ turbidity data using cubic polynomial regression equations. The results indicated that the T s , which uses the only red-edge wavelength, identified turbidity as the most accurate across all locations (highest R2 = 0.91, lowest RMSE = 0.003), followed by NDTI (highest R2 = 0.85, lowest RMSE = 0.05), respectively. The remote sensing data application provides a better way to monitor turbidity at large spatio-temporal scales in attaining the water quality standards of the Godavari River.

Toward Nano- and Microplastic Sensors: Identification of Nano- and Microplastic Particles via Artificial Intelligence Combined with a Plasmonic Probe Functionalized with an Estrogen Receptor

Nano- and microplastic particles are a global and emerging environmental issue that might pose potential threats to human health. The present work exploits artificial intelligence (AI) to identify nano- and microplastics in water by monitoring the interaction of the sample with a sensitive surface. An estrogen receptor (ER) grafted onto a gold surface, realized on a nonexpensive and easy-to-produce plastic optical fiber (POF) platform in order to excite a surface plasmon resonance (SPR) phenomenon, has been developed in order to carry out a "smart" sensitive interface (ER-SPR-POF interface). The ER-SPR-POF interface offers output data useful for exploiting a machine learning-based approach to achieve nano- and microplastic particle sensors. This work developed a proof-of-concept sensor through a training phase carried out by different particles, in terms of materials and size. The experimental results have demonstrated that the proposed "smart" ER-SPR-POF interface combined with AI can be used to identify the kind of particles in terms of the materials (polystyrene; poly(methyl methacrylate)) and size (20 μm; 100 nm) with an accuracy of 90.3%.

Application of machine learning techniques to predict groundwater quality in the Nabogo Basin, Northern Ghana

The main objective of this study was to map the quality of groundwater for domestic use in the Nabogo Basin, a sub-catchment of the White Volta Basin in Ghana, by applying machine learning techniques. The study was conducted by applying the Random Forest (RF) machine learning algorithm to predict groundwater quality, by utilizing factors that influence groundwater occurrence and quality such as Elevation, Topographical Wetness Index (TWI), Slope length (LS), Lithology, Soil type, Normalize Different Vegetation Index (NDVI), Rainfall, Aspect, Slope, Plan Curvature (PLC), Profile Curvature (PRC), Lineament density, Distance to faults, and Drainage density. The groundwater quality of the area was predicted by building a Random Forest model based on computed Arithmetic Water Quality Indices (WQI) (as dependent variable) of existing boreholes, to serve as an indicator of the groundwater quality. The predicted WQI of groundwater in the study area shows that it ranges from 9.51 to 69.99%. This implied that 21.97 %, 74.40 %, and 3.63 % of the study area had respectively the likelihood of excellent. The models were found to perform much better with an RMSE of 23.03 and an R2 value of 0.82. The study conducted highlighted an essential understanding of the groundwater quality in the study area, paving the way for further studies and policy development for groundwater management.

Development of AI-based hybrid soft computing models for prediction of critical river water quality indicators

Prediction of river water quality indicators (RWQIs) using artificial intelligence (AI)-based hybrid soft computing modeling techniques could provide essential predictions required for efficient river health planning and management. The study described the development of a novel AI-based relative weighted ensemble (AIRWE) hybrid model for predicting critical RWQIs, i.e., biochemical oxygen demand (BOD) and total coliform (TC). The study involved comprehensive water quality (WQ) monitoring from 30 locations along the Damodar River to establish the baseline data and delineate the WQ. The representative input features showing a strong association with BOD and TC were identified using Spearman's rank-coupled orthogonal linear transformation (SOT). The relative weighted ensemble (RWE) method was applied to determine the relative weights for base learners in the AIRWE model. The statistical analysis of the developed model revealed that it was most efficient and accurate for predicting BOD (R2, 0.97; RMSE, 0.06; MAE, 0.04) and TC (R2, 0.98; RMSE, 0.06; MAE, 0.05) over the traditional techniques. The tstat (BOD 0.02 and TC 0.47) was lesser than tcrit (1.672), confirming its unbiased predictions. The SOT technique removed the data noise and multicollinearity, whereas RWE curtailed the individual model's limitations and predicted more reliable results. The model resulted 97% accuracy with high precision (96%) in classifying the river water quality for various end uses. The study describes a novel approach for researchers, scientists, and decision-makers for modeling and predicting various environmental attributes.

Advances in machine learning and IoT for water quality monitoring: A comprehensive review

Water holds great significance as a vital resource in our everyday lives, highlighting the important to continuously monitor its quality to ensure its usability. The advent of the. The Internet of Things (IoT) has brought about a revolutionary shift by enabling real-time data collection from diverse sources, thereby facilitating efficient monitoring of water quality (WQ). By employing Machine learning (ML) techniques, this gathered data can be analyzed to make accurate predictions regarding water quality. These predictive insights play a crucial role in decision-making processes aimed at safeguarding water quality, such as identifying areas in need of immediate attention and implementing preventive measures to avert contamination. This paper aims to provide a comprehensive review of the current state of the art in water quality monitoring, with a specific focus on the employment of IoT wireless technologies and ML techniques. The study examines the utilization of a range of IoT wireless technologies, including Low-Power Wide Area Networks (LpWAN), Wi-Fi, Zigbee, Radio Frequency Identification (RFID), cellular networks, and Bluetooth, in the context of monitoring water quality. Furthermore, it explores the application of both supervised and unsupervised ML algorithms for analyzing and interpreting the collected data. In addition to discussing the current state of the art, this survey also addresses the challenges and open research questions involved in integrating IoT wireless technologies and ML for water quality monitoring (WQM).

Long-term (2003-2021) evolution trend of water quality in the Three Gorges Reservoir: An evaluation based on an enhanced water quality index

The degradation of water quality induced by the construction of large-scale hydraulic projects is one of the primary public concerns; however, it is rarely addressed with long-term field observation data. Here, we reported the long-term (2003-2021) trends, seasonal patterns, and overall condition of water quality of the Three Gorges Reservoir (TGR) with an enhanced water quality index (WQI). Specifically, to emphasize the importance of the biological role in water quality assessment, chlorophyll-a (Chla) was incorporated into WQI, and then a novel workflow using machine learning approach based on Random Forest (RF) model was constructed to develop a minimal water quality index (WQImin). The enhanced WQI indicated an overall "good" water quality condition, exhibiting a gradually improving trend subsequent to the reservoir impoundment in 2003. Meanwhile, the assessment revealed that the water quality has discernible seasonal patterns, characterized by poorer conditions in the spring and summer seasons. Furthermore, the RF model identified Chla, dissolved oxygen (DO), ammonium nitrogen (NH4-N), water temperature (WT), pH, and total nitrogen (TN) as key parameters for the WQImin, with Chla emerging as the most important factor in determining WQImin in our study. Moreover, weighted WQImin models exhibited improved performance in estimating WQI. Our study emphasizes the importance of biological parameters in water quality assessment, and introduces a systematic workflow to facilitate the development of WQImin for accurate and cost-efficient water quality assessment. Furthermore, our study makes a substantial contribution to the advancement of knowledge regarding long-term trends and seasonal patterns in water quality of large reservoirs, which provides a foundational basis for guiding water quality management practices for reservoirs worldwide.

Coastal groundwater quality prediction using objective-weighted WQI and machine learning approach

The water quality index (WQI) is a globally accepted guideline to indicate the water quality standard of any groundwater resource. Water levels in existing groundwater sources are declining in several coastal zones. Therefore, for monitoring water quality and improving water management, the prediction and identification of groundwater status by an effective technique with higher accuracy is urgently needed. Therefore, this research aims to find an effective model for WQI prediction by comparing entropy and critic weight-based WQI (ENW-WQI and CRITIC-WQI) with multi-layer perceptron artificial neural network (MLP-ANN) technique and also to identify contaminated zones using GIS. Initially, 1000 water sampling datasets with concentrations of several water quality parameters of different coastal blocks of eastern India during 2018 to 2022 are considered for the estimation of ENW-WQI and CRITIC-WQI. It shows 65% and 67% of the samples are excellent to good for drinking. ENW-WQI and CRITIC-WQI-based MLP-ANN models have been established considering different data portioning and hidden neuron numbers. Input variables and appropriate dataset partitioning with hidden neurons for models obtained from correlation and trial-error analysis. Spatial distribution maps are also produced for calculated WQIs using inverse distance weighted interpolation approaches. Three fitting models are obtained: ENW-WQI-MLP-ANN, CRITIC-WQI-MLP-ANN-I and CRITIC-WQI-MLP-ANN-II. CRITIC-WQI-MLP-ANN-II model (data ratio 85:15, network structure 6-12-1, R2 = 0.986, NSE = 0.98, and error rate 0.49%) provides the best accuracy in WQI prediction. The GIS-based WQI maps record several areas related to drinking water quality. The results of this research can help in planning the provision of safe drinking water in the future.

Prediction of weighted arithmetic water quality index for urban water quality using ensemble machine learning model

Urban water quality index (WQI) is an important factor for assessment quality of groundwater in the urban and rural area. In this research, the Weighted Arithmetic Water Quality Index (WA-WQI) was estimated for understanding the groundwater quality. Four machine learning (ML) models were developed including artificial neural network (ANN), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XG-Boost) in addition to multiple linear regression (MLR) for WA-WQI prediction at the Ujjain city of Madhya Pradesh in India. Groundwater quality samples were collected from 54 wards under the urban area, the main eight different physiochemical parameters were selected for WA-WQI prediction. The different input parameters data were analysed and calculated for the relationships of their ability to predict the results of WA-WQI. The ML models performance were calculated using three statistical metrics such as determination coefficient (R2), mean absolute error (MAE), and root mean square error (RMSE). In this research shown the XG-Boost model is better results other than other ML models. The best modelling results over the training phase revealed R2 = 0.969, RMSE = 2.169, MAE = 2.013 and over the testing phase R2 = 0.987, RMSE = 3.273, MAE = 2.727). All the ML models results were validated using receiver operating characteristic (ROC) curve for the best models selection. The results of best model area under curve (AUC) was 0.9048. Hence, XG-Boost model was given the accurate prediction of WA-WQI in the urban area. Based on the graphical presentation evaluation, XG-Boost model showed similar results of superiority. The obtained modelling results emphasis the utility of computer aid models for better planning and essential information for decision-makers, and water experts. The implement agency can adopt the procedures of water quality to decrease pollution and safe and healthy water provide to entire Ujjain city.

HDTO-DeepAR: A novel hybrid approach to forecast surface water quality indicators

Water is a vital resource supporting a broad spectrum of ecosystems and human activities. The quality of river water has declined in recent years due to the discharge of hazardous materials and toxins. Deep learning and machine learning have gained significant attention for analysing time-series data. However, these methods often suffer from high complexity and significant forecasting errors, primarily due to non-linear datasets and hyperparameter settings. To address these challenges, we have developed an innovative HDTO-DeepAR approach for predicting water quality indicators. This proposed approach is compared with standalone algorithms, including DeepAR, BiLSTM, GRU and XGBoost, using performance metrics such as MAE, MSE, MAPE, and NSE. The NSE of the hybrid approach ranges between 0.8 to 0.96. Given the value's proximity to 1, the model appears to be efficient. The PICP values (ranging from 95% to 98%) indicate that the model is highly reliable in forecasting water quality indicators. Experimental results reveal a close resemblance between the model's predictions and actual values, providing valuable insights for predicting future trends. The comparative study shows that the suggested model surpasses all existing, well-known models.

An innovative approach for predicting groundwater TDS using optimized ensemble machine learning algorithms at two levels of modeling strategy

Groundwater salinization in coastal aquifers is a major socioeconomic challenge in Oman and many other regions worldwide due to several anthropogenic activities and natural drivers. Therefore, assessing the salinization of groundwater resources is crucial to ensure the protection of water resources and sustainable management. The aim of this study is to apply a novel approach using predictive optimized ensemble trees-based (ETB) machine learning models, namely Catboost regression (CBR), Extra trees regression (ETR), and Bagging regression (BA), at two levels of modeling strategy for predicting groundwater TDS as an indicator for seawater intrusion in a coastal aquifer, Oman. At level 1, ETR and CBR models were used as base models or inputs for BA in level 2. The results show that the models at level 1 (i.e., ETR and CBR) yielded satisfactory results using a limited number of inputs (Cl, K, and Sr) from a few sets of 40 groundwater wells. The BA model at level 2 improved the overall performance of the modeling by extracting more information from ETR and CBR models at level 1 models. At level 2, the BA model achieved a significant improvement in accuracy (MSE = 0.0002, RSR = 0.062, R2 = 0.995 and NSE = 0.996) compared to each individual model of ETR (MSE = 0.0007, RSR = 0.245, R2 = 0.98 and NSE = 0.94), and CBR (MSE = 0.0035, RSR = 0.258, R2 = 0.933 and NSE = 0.934) at level 1 models in the testing dataset. BA model at level 2 outperformed all models regarding predictive accuracy, best generalization of new data, and matching the locations of the polluted and unpolluted wells. Our approach predicts groundwater TDS with high accuracy and thus provides early warnings of water quality deterioration along coastal aquifers which will improve water resources sustainability.

>Water quality prediction of artificial intelligence model: a case of Huaihe River Basin, China

Accurate prediction of water quality contributes to the intelligent management of water resources. Water quality indices have time series characteristics and nonlinearity, but the existing models only focus on the forward time series when long short-term memory (LSTM) is introduced and do not consider the parallel computation on the model. Owing to this, a new neural network called LSTM-multihead attention (LMA) was constructed to predict water quality, using long short-term memory to process time series data and multihead attention for parallel computing and extracting feature information. Additionally, water quality indices have the issues of multiple data types and complex data correlations, as well as missing data and abnormal data problems in water quality data. In order to solve these problems, this study proposes a water quality prediction model called GRA-LMA-based linear interpolation, gray relational analysis and LMA. Two experiments are carried out to verify the predictive performance of the GRA-LMA with the water quality data of the Huaihe River Basin as a case study sample. The first experiment focuses on data processing, including the processing of missing data and abnormal data of water quality data, and the correlation analysis of water quality indices. Linear interpolation is adapted to process the missing data, while a combination of boxplot and histogram is adopted to analyze and eliminate the abnormal data, which is then repaired the abnormal data with linear interpolation. The gray relational analysis is adopted to calculate the correlation between different water quality indices, and water quality indices with high correlation are retained to determine the input variables of the water quality prediction model. The data processing results demonstrate that repairs can be made using linear interpolation without altering the pattern of data change and the model by using the gray relational analysis to reduce the quantity of data it needs as input. In the second experiment, the predictive capacity of GRA-LMA and existing models such as backpropagation neural network (BP), recurrent neural network (RNN), long short-term memory (LSTM), and gate recurrent unit (GRU) was evaluated and compared using different numerical and graphical performance evaluation metrics. Comparative experimental results show that the mean square error of pH, dissolved oxygen, chemical oxygen demand, ammonia nitrogen, electrical conductivity, turbidity, total phosphorus, and total nitrogen of GRA-LMA is reduced to 0.05890, 0.40196, 0.32454, 0.04368, 14.71003, 8.13252, 0.01558, and 0.14345. The results indicate that GRA-LMA has superior adaptability for predicting various water quality indices and can significantly lower the induced prediction error.

Optimisation and interpretation of machine and deep learning models for improved water quality management in Lake Loktak

Loktak Lake, one of the largest freshwater lakes in Manipur, India, is critical for the eco-hydrology and economy of the region, but faces deteriorating water quality due to urbanisation, anthropogenic activities, and domestic sewage. Addressing the urgent need for effective pollution management, this study aims to assess the lake's water quality status using the water quality index (WQI) and develop advanced machine learning (ML) tools for WQI assessment and ML model interpretation to improve pollution management decision making. The WQI was assessed using entropy-based weighting arithmetic and three ML models - Gradient Boosting Machine (GBM), Random Forest (RF) and Deep Neural Network (DNN) - were optimised using a grid search algorithm in the H2O Application Programming Interface (API). These models were validated by various metrics and interpreted globally and locally via Partial Dependency Plot (PDP), Accumulated Local Effect (ALE) and SHapley Additive exPlanations (SHAP). The results show a WQI range of 72.38-100, with 52.7% of samples categorised as very poor. The RF model outperformed GBM and DNN and showed the highest accuracy and generalisation ability, which is reflected in the superior R2 values (0.97 in training, 0.9 in test) and the lower root mean square error (RMSE). RF's minimal margin of error and reliable feature interpretation contrasted with DNN's larger margin of error and inconsistency, which affected its usefulness for decision making. Turbidity was found to be a critical predictive feature in all models, significantly influencing WQI, with other variables such as pH and temperature also playing an important role. SHAP dependency plots illustrated the direct relationship between key water quality parameters such as turbidity and WQI predictions. The novelty of this study lies in its comprehensive approach to the evaluation and interpretation of ML models for WQI estimation, which provides a nuanced understanding of water quality dynamics in Loktak Lake. By identifying the most effective ML models and key predictive functions, this study provides invaluable insights for water quality management and paves the way for targeted strategies to monitor and improve water quality in this vital freshwater ecosystem.

Data-driven modelling for assessing trophic status in marine ecosystems using machine learning approaches

Assessing eutrophication in coastal and transitional waters is of utmost importance, yet existing Trophic Status Index (TSI) models face challenges like multicollinearity, data redundancy, inappropriate aggregation methods, and complex classification schemes. To tackle these issues, we developed a novel tool that harnesses machine learning (ML) and artificial intelligence (AI), enhancing the reliability and accuracy of trophic status assessments. Our research introduces an improved data-driven methodology specifically tailored for transitional and coastal (TrC) waters, with a focus on Cork Harbour, Ireland, as a case study. Our innovative approach, named the Assessment Trophic Status Index (ATSI) model, comprises three main components: the selection of pertinent water quality indicators, the computation of ATSI scores, and the implementation of a new classification scheme. To optimize input data and minimize redundancy, we employed ML techniques, including advanced deep learning methods. Specifically, we developed a CHL prediction model utilizing ten algorithms, among which XGBoost demonstrated exceptional performance, showcasing minimal errors during both training (RMSE = 0.0, MSE = 0.0, MAE = 0.01) and testing (RMSE = 0.0, MSE = 0.0, MAE = 0.01) phases. Utilizing a novel linear rescaling interpolation function, we calculated ATSI scores and evaluated the model's sensitivity and efficiency across diverse application domains, employing metrics such as R2, the Nash-Sutcliffe efficiency (NSE), and the model efficiency factor (MEF). The results consistently revealed heightened sensitivity and efficiency across all application domains. Additionally, we introduced a brand new classification scheme for ranking the trophic status of transitional and coastal waters. To assess spatial sensitivity, we applied the ATSI model to four distinct waterbodies in Ireland, comparing trophic assessment outcomes with the Assessment of Trophic Status of Estuaries and Bays in Ireland (ATSEBI) System. Remarkably, significant disparities between the ATSI and ATSEBI System were evident in all domains, except for Mulroy Bay. Overall, our research significantly enhances the accuracy of trophic status assessments in marine ecosystems. The ATSI model, combined with cutting-edge ML techniques and our new classification scheme, represents a promising avenue for evaluating and monitoring trophic conditions in TrC waters. The study also demonstrated the effectiveness of ATSI in assessing trophic status across various waterbodies, including lakes, rivers, and more. These findings make substantial contributions to the field of marine ecosystem management and conservation.

Prediction of drinking water quality with machine learning models: A public health nursing approach

OBJECTIVE

The aim of this study is to use machine learning models to predict drinking water quality from a public health nursing approach.

DESIGN

Machine learning study.

SAMPLE

"Water Quality Dataset" was used in the study. The dataset contains physical and chemical measurements of water quality for 2400 different water bodies. The process consists of four stages: Data processing with Synthetic Minority Oversampling Technique, hyperparameter tuning with 10-fold cross-validation, modeling and comparative analysis. 80% of the dataset is allocated as training data and 20% as test data. ML models logistic regression, K-nearest neighbor, support vector machine, random forest, XGBoost, AdaBoost Classifier, Decision Tree algorithms were used for water quality prediction. Accuracy, precision, recall, F1 score and AUC performance metrics of ML models were compared. To evaluate the performance of the models, 10-fold cross-validation was used and a comparative analysis was performed. The p-values of the models were also compared.

RESULTS

N this study, where drinking water quality was predicted with seven different ML algorithms, it can be said that XGBoost and Random Forest are the best classification models in all performance metrics. There is a significant difference in all ML algorithms according to the p-value. The H0 hypothesis is accepted for these algorithms. According to the H0 hypothesis, there is no difference between actual values and predicted values.

CONCLUSION

In conclusion, the use of ML models in the prediction of drinking water quality can help nurses greatly improve access to clean water, a human right, be more knowledgeable about water quality, and protect the health of individuals.

Forecasting water quality variable using deep learning and weighted averaging ensemble models

Water quality variables, including chlorophyll-a (Chl-a), play a pivotal role in comprehending and evaluating the condition of aquatic ecosystems. Chl-a, a pigment present in diverse aquatic organisms, notably algae and cyanobacteria, serves as a valuable indicator of water quality. Thus, the objectives of this study encompass: (1) the assessment of the predictive capabilities of four deep learning (DL) models - namely, recurrent neural network (RNN), long short-term memory (LSTM), gated recurrence unit (GRU), and temporal convolutional network (TCN) - in forecasting Chl-a concentrations; (2) the incorporation of these DL models into ensemble models (EMs) employing genetic algorithm (GA) and non-dominated sorting genetic algorithm (NSGA-II) to harness the strengths of each standalone model; and (3) the evaluation of the efficacy of the developed EMs. Utilizing data collected at 15-min intervals from Small Prespa Lake (SPL) in Greece, the models employed hourly Chl-a concentration lag times, extending up to 6 h, as models' inputs to forecast Chla (t+1). The proposed models underwent training on 70% of the dataset and were subsequently validated on the remaining 30%. Among the standalone DL models, the GRU model exhibited superior performance in Chl-a forecasting, surpassing the RNN, LSTM, and TCN models by 8%, 2%, and 2%, respectively. Furthermore, the integration of DL models through single-objective GA and multi-objective NSGA-II optimization algorithms yielded hybrid models adept at effectively forecasting both low and high Chl-a concentrations. The ensemble model based on NSGA-II outperformed standalone DL models as well as the GA-based model across a range of evaluation indices. For instance, considering the R-squared metric, the study's findings demonstrated that the EM-NSGA-II stands out with exceptional effectiveness compared to DL and EM-GA models, showcasing improvements of 14% (RNN), 8% (LSTM), 6% (GRU), 8% (TCN), and 3% (EM-GA) during the testing phase.

Comparison between the WFD approaches and newly developed water quality model for monitoring transitional and coastal water quality in Northern Ireland

This study aims to evaluate existing approaches for monitoring and assessing water quality in waterbodies in the North of Ireland using newly developed methodologies. The results reveal significant differences between the new technique and the existing "one-out, all-out" approach in rating water quality. The new approach found the water quality status to be "good," "fair," and "marginal," whereas the existing "one-out, all-out" technique classified water quality as "good," and "moderate," respectively. The new technique outperformed existing approaches in rating the water quality of different waterbody types, with high R2 = 1, NSE = 0.99, and MEF = 0 values. Furthermore, the final assessment of water quality using the new methodologies had the lowest uncertainty (<1 %), whereas the efficiency measures (NSE and MEF) indicate that the new approaches are bias-free to assess water quality at any geographic scale. The results of this study reveal that the newly proposed methodologies are effective in assessing the water quality states of transitional and coastal waterbodies in the North of Ireland. The study also highlighted the limitations of existing approaches and the importance of updating water resource management systems for better protection of these waterbodies. The findings have significant implications for water resource management and planning in the North of Ireland and other similar regions.

Assessing the impact of COVID-19 lockdown on surface water quality in Ireland using advanced Irish water quality index (IEWQI) model

The COVID-19 pandemic has significantly impacted various aspects of life, including environmental conditions. Surface water quality (WQ) is one area affected by lockdowns imposed to control the virus's spread. Numerous recent studies have revealed the considerable impact of COVID-19 lockdowns on surface WQ. In response, this research aimed to assess the impact of COVID-19 lockdowns on surface water quality in Ireland using an advanced WQ model. To achieve this goal, six years of water quality monitoring data from 2017 to 2022 were collected for nine water quality indicators in Cork Harbour, Ireland, before, during, and after the lockdowns. These indicators include pH, water temperature (TEMP), salinity (SAL), biological oxygen demand (BOD5), dissolved oxygen (DOX), transparency (TRAN), and three nutrient enrichment indicators-dissolved inorganic nitrogen (DIN), molybdate reactive phosphorus (MRP), and total oxidized nitrogen (TON). The results showed that the lockdown had a significant impact on various WQ indicators, particularly pH, TEMP, TON, and BOD5. Over the study period, most indicators were within the permissible limit except for MRP, with the exception of during COVID-19. During the pandemic, TON and DIN decreased, while water transparency significantly improved. In contrast, after COVID-19, WQ at 7% of monitoring sites significantly deteriorated. Overall, WQ in Cork Harbour was categorized as "good," "fair," and "marginal" classes over the study period. Compared to temporal variation, WQ improved at 17% of monitoring sites during the lockdown period in Cork Harbour. However, no significant trend in WQ was observed. Furthermore, the study analyzed the advanced model's performance in assessing the impact of COVID-19 on WQ. The results indicate that the advanced WQ model could be an effective tool for monitoring and evaluating lockdowns' impact on surface water quality. The model can provide valuable information for decision-making and planning to protect aquatic ecosystems.

Application of machine learning in river water quality management: a review

Machine learning (ML), a branch of artificial intelligence (AI), has been increasingly used in environmental engineering due to the ability to analyze complex nonlinear problems (such as ones connected with water quality management) through a data-driven approach. This study provides an overview of different ML algorithms applied for monitoring and predicting river water quality. Different parameters could be monitored or predicted, such as dissolved oxygen (DO), biological and chemical oxygen demand (BOD and COD), turbidity levels, the concentration of different ions (such as Mg2+ and Ca2+), heavy metal or other pollutant's concentration, pH, temperature, and many more. Although many algorithms have been investigated for the prediction of river water quality, there are several which are most commonly used in engineering practice. These models mostly include so-called supervised learning algorithms, such as artificial neural network (ANN), support vector machine (SVM), random forest (RF), decision tree (DT), and deep learning (DL). To further enhance prediction power, novel hybrid algorithms, could be used. However, the quality of prediction is not only dependent on the applied algorithm but also on the availability of previously mentioned water quality parameters, their selection, and the combination of input data used to train the ML model.

A holistic approach for understanding the status of water quality and causes of its deterioration in a drought-prone agricultural area of Southeastern India

This study investigates the groundwater quality in the Kadiri Basin, Ananthapuramu district of Andhra Pradesh, India. Groundwater samples from 77 locations were collected and tested for the concentration of various physicochemical parameters. The collected data were assimilated in the form of a groundwater quality index to estimate groundwater quality (drinking and irrigation) using an information entropy-based weight determination approach (EWQI). The water quality maps obtained from the study area suggest a definite trend in groundwater contamination of the study area. Furthermore, the influence of different physicochemical parameters on groundwater quality was determined using machine learning techniques. Learning and prediction accuracies of four different techniques, namely artificial neural network (ANN), deep learning (DL), random forest (RF), and gradient boosting machine (GBM), were investigated. The performance of the ANN model (MEA = 11.23, RSME = 21.22, MAPE = 7.48, and R2 = 0.91) was found to be highly effective for the present dataset. The ANN model was then used to understand the relative influence of physicochemical parameters on groundwater quality. It was observed that the deterioration in groundwater quality in the study area was primarily due to the excess concentration of turbidity and iron values. The relatively higher concentration of sulfate and nitrate had caused a significant impact on the groundwater quality. The study has wider implications for modeling in similar drought-prone agricultural areas elsewhere for assessing the groundwater quality.

Impact of meteorological parameters on soil radon at Kolkata, India: investigation using machine learning techniques

The daily soil radon activity has been measured continuously over a year with BARASOL BMC2 probe at a measuring site of Jadavpur University Campus in Kolkata, India. The dependency of soil radon activity with different atmospheric parameters such as soil temperature, soil pressure, humidity, air temperature, and rainfall has been also analyzed. The whole study period is divided in four seasons as proposed by the Indian Meteorological Department (IMD). Minimum soil radon level has been observed during the winter season (December-February). On the other hand, higher soil radon level has been observed both for summer and monsoon. Except soil pressure, all other variables have shown positive correlation with soil radon activity. Among five variables, soil temperature has been the most significant variable in terms of correlation with soil radon level whereas maximum humidity has been the least significant correlated variable. It has been observed that considerable reduction of soil radon level occurred after four heavy rainfall events during the study period. The combined effect of these multi-parameters on soil radon gas has been evaluated using machine learning methods like principal component regression (PCR), support vector regression (SVR), random forest regression (RF), and gradient boosting machine (GBM). In terms of performances, RF and GBM have performed much better than SVR and PCR. More robust and consistent results have been obtained for GBM during both training and testing periods.

Machine learning approach for the estimation of missing precipitation data: a case study of South Korea

Precipitation is one of the driving forces in water cycles, and it is vital for understanding the water cycle, such as surface runoff, soil moisture, and evapotranspiration. However, missing precipitation data at the observatory becomes an obstacle to improving the accuracy and efficiency of hydrological analysis. To address this issue, we developed a machine learning algorithm-based precipitation data recovery tool to detect and predict missing precipitation data at observatories. This study investigated 30 weather stations in South Korea, evaluating the applicability of machine learning algorithms (artificial neural network and random forest) for precipitation data recovery using environmental variables, such as air pressure, temperature, humidity, and wind speed. The proposed model showed a high performance in detecting the missing precipitation occurrence with an accuracy of 80%. In addition, the prediction results from the models showed predictive ability with a correlation coefficient ranging from 0.5 to 0.7 and R2 values of 0.53. Although both algorithms performed similarly in estimating precipitation, ANN performed slightly better. Based on the results of this study, we expect that the machine learning algorithms can contribute to improving hydrological modeling performance by recovering missing precipitation data at observation stations.

Proposal for a new customization process for a data-based water quality index using a random forest approach

As the water quality index (WQI) represents water quality, it is crucial to customize the WQI for a specific purpose. In this study, to better represent water quality data using WQI, a random forest (RF) approach was used to derive the parameter weight and calculate the WQI according to the watershed and its use. Eight parameters (water temperature, dissolved oxygen, pH, electrical conductivity, suspended solids, total nitrogen, total phosphorus, and total organic carbon) were evaluated using a total of 220,103 data points collected from 900 monitoring sites throughout South Korea between 2011 and 2020. The estimation of parameter weights, key elements in developing the WQI model, was performed through the variable importance estimation method that can be derived from the RF model. The parameter weights were derived based on various spatiotemporal datasets, and it was confirmed that the spatiotemporal differences in weights according to data characteristics represented the regional and seasonal water quality characteristics. Consequently, a customized WQI representing water quality characteristics could be calculated using data-based weights, and it is expected that a data-based customized WQI could be developed to better match the previous WQI to the purpose and target source.

Deep learning approach for prediction and classification of potable water

Potable water, commonly known as drinking water, refers to water that is safe to drink and does not endanger human health. It must adhere to strict quality standards set by health organizations, be devoid of dangerous pollutants and chemicals, and meet certain requirements for safety. The health of the public and the ecosystem are directly affected by water quality. Various pollutants have posed dangers to water quality in recent years. A more efficient and affordable approach is required due to the grave effects of low water quality. In this proposed research work, deep learning algorithms are developed to predict the water quality index (WQI) and water quality classifications (WQC), which are vital parameters that can be utilized to know the status of the water. To predict the WQI, a deep learning algorithm called long short-term memory (LSTM) is used. Further, WQC is performed using a deep learning algorithm called a convolutional neural network (CNN). The proposed system considers seven water quality parameters, namely, dissolved oxygen (DO), pH, conductivity, biological oxygen demand (BOD), nitrate, fecal coliform, and total coliform. The experimental results showed that the LSTM can predict water quality with superior robustness and predict WQI with the highest accuracy of 97%. Similarly, the CNN model classifies the WQC as potable or impotable with superior accuracy and a reduced error rate of 0.02.

Large-scale prediction of stream water quality using an interpretable deep learning approach

Deep learning methods, which have strong capabilities for mapping highly nonlinear relationships with acceptable calculation speed, have been increasingly applied for water quality prediction in recent studies. However, it is argued that the practicality of deep learning methods is limited due to the lack of physical mechanics to explain the prediction results of water quality changes. A knowledge gap exists in rationalizing the deep learning results for water quality predictions. To address this gap, an interpretable deep learning framework was established to predict the spatiotemporal variations of water quality parameters in a large spatial region. Mereological, land-use, and socioeconomic variables were adopted to predict the daily variations of stream water quality parameters across 138 sub-catchments in a total of over 575,250 km² in southern China. The coefficients of determination of chemical oxygen demand (COD), total phosphorus (TP), and total nitrogen (TN) predictions were over 0.80, suggesting a satisfactory prediction performance. The model performance in terms of prediction accuracy could be improved by involving land-use and socioeconomic predictors in addition to hydrological variables. The SHapley Additive exPlanations method used in this study was demonstrated to be effective for interpreting the prediction results by identifying the significant variables and reasoning their influencing directions on the variation of each water quality parameter. The air temperature, proportion of forest area, grain production, population density, and proportion of urban area in each sub-catchment as well as the accumulated rainfall within the previous 3 days were identified as the most significant variables affecting the variations of dissolved oxygen, COD, ammoniacal nitrogen(NH₃-N), TN, TP, and turbidity in the stream water in the case area, respectively.

Advances in Catchment Science, Hydrochemistry, and Aquatic Ecology Enabled by High-Frequency Water Quality Measurements

High-frequency water quality measurements in streams and rivers have expanded in scope and sophistication during the last two decades. Existing technology allows in situ automated measurements of water quality constituents, including both solutes and particulates, at unprecedented frequencies from seconds to subdaily sampling intervals. This detailed chemical information can be combined with measurements of hydrological and biogeochemical processes, bringing new insights into the sources, transport pathways, and transformation processes of solutes and particulates in complex catchments and along the aquatic continuum. Here, we summarize established and emerging high-frequency water quality technologies, outline key high-frequency hydrochemical data sets, and review scientific advances in key focus areas enabled by the rapid development of high-frequency water quality measurements in streams and rivers. Finally, we discuss future directions and challenges for using high-frequency water quality measurements to bridge scientific and management gaps by promoting a holistic understanding of freshwater systems and catchment status, health, and function.

Evaluating the combined effect of climate and anthropogenic stressors on marine coastal ecosystems: Insights from a systematic review of cumulative impact assessment approaches

Cumulative impacts increasingly threaten marine and coastal ecosystems. To address this issue, the research community has invested efforts on designing and testing different methodological approaches and tools that apply cumulative impact appraisal schemes for a sound evaluation of the complex interactions and dynamics among multiple pressures affecting marine and coastal ecosystems. Through an iterative scientometric and systematic literature review, this paper provides the state of the art of cumulative impact assessment approaches and applications. It gives a specific attention to cutting-edge approaches that explore and model inter-relations among climatic and anthropogenic pressures, vulnerability and resilience of marine and coastal ecosystems to these pressures, and the resulting changes in ecosystem services flow. Despite recent advances in computer sciences and the rising availability of big data for environmental monitoring and management, this literature review evidenced that the implementation of advanced complex system methods for cumulative risk assessment remains limited. Moreover, experts have only recently started integrating ecosystem services flow into cumulative impact appraisal frameworks, but more as a general assessment endpoint within the overall evaluation process (e.g. changes in the bundle of ecosystem services against cumulative impacts). The review also highlights a lack of integrated approaches and complex tools able to frame, explain, and model spatio-temporal dynamics of marine and coastal ecosystems' response to multiple pressures, as required under relevant EU legislation (e.g., Water Framework and Marine Strategy Framework Directives). Progress in understanding cumulative impacts, exploiting the functionalities of more sophisticated machine learning-based approaches (e.g., big data integration), will support decision-makers in the achievement of environmental and sustainability objectives.

A novel approach for estimating and predicting uncertainty in water quality index model using machine learning approaches

With the significant increase in WQI applications worldwide and lack of specific application guidelines, accuracy and reliability of WQI models is a major issue. It has been reported that WQI models produce significant uncertainties during the various stages of their application including: (i) water quality indicator selection, (ii) sub-index (SI) calculation, (iii) water quality indicator weighting and (iv) aggregation of sub-indices to calculate the overall index. This research provides a robust statistically sound methodology for assessment of WQI model uncertainties. Eight WQI models are considered. The Monte Carlo simulation (MCS) technique was applied to estimate model uncertainty, while the Gaussian Process Regression (GPR) algorithm was utilised to predict uncertainties in the WQI models at each sampling site. The sub-index functions were found to contribute to considerable uncertainty and hence affect the model reliability - they contributed 12.86% and 10.27% of uncertainty for summer and winter applications, respectively. Therefore, the selection of sub-index function needs to be made with care. A low uncertainty of less than 1% was produced by the water quality indicator selection and weighting processes. Significant statistical differences were found between various aggregation functions. The weighted quadratic mean (WQM) function was found to provide a plausible assessment of water quality of coastal waters at reduced uncertainty levels. The findings of this study also suggest that the unweighted root means squared (RMS) aggregation function could be potentially also used for assessment of coastal water quality. Findings from this research could inform a range of stakeholders including decision-makers, researchers, and agencies responsible for water quality monitoring, assessment and management.

What will the water quality of the Yangtze River be in the future?

The long-term prediction of water quality is important for water pollution control planning and water resource management, but it has received little attention. In this study, the water quality trend in the Yangtze River is found to stabilize at most monitoring stations under environmental protection activities. Based on the physical mechanism and stochastic theory, a novel river water quality prediction model combining pollution source decomposition (including local point, local nonpoint and upstream sources) and time series decomposition (including trend, seasonal and residential components) is developed. The observed water quality data from 76 monitoring stations in the Yangtze River, including permanganate index (COD_Mn) and total phosphorus (TP), are used to drive this model to make long-term water quality predictions. The results show that this model has an acceptable accuracy. In the future, the concentration of COD_Mn will meet the water quality targets at most stations in the Yangtze River, but the concentration of TP will not be able to meet the water quality target at 28.5 % of the stations. Furthermore, the prediction value of COD_Mn is 62.2 % lower than the target on average. However, the prediction value of TP is only 24.4 % lower than the target on average, and it will exceed the water target by >50 % at some stations. This model has the potential to be widely used for long-term water quality prediction in the future.

Estimation and uncertainty analysis of groundwater quality parameters in a coastal aquifer under seawater intrusion: a comparative study of deep learning and classic machine learning methods

Excessive withdrawal of groundwater for agricultural irrigation can cause seawater intrusion into coastal aquifers. Such a case will in turn results in deterioration of irrigation water quality. Determination of irrigation water quality with traditional methods is a time-consuming and costly process. However, machine learning algorithms can be useful tools for modeling and estimating groundwater quality used for irrigation water purposes. In this study, TDS, PS, SAR, and Cl parameters of groundwater were estimated with models based on EC and pH variables. For this purpose, prediction performances of two different deep learning methods (convolutional neural network (CNN) and deep neural network (DNN)) and two different classical machine learning (Random Forest (RF) and extreme gradient boosting (XGBoost)) methods were compared. In addition, predictive uncertainty of the models was determined by quantile regression (QR) analysis. Performance criteria and results of uncertainty analysis revealed that CNN (in testing phase, NSE = 0.95 for TDS, NSE = 0.96 for PS, NSE = 0.67 for SAR and NSE = 0.93 for CI) and DNN (in testing phase, NSE = 0.91 for TDS, NSE = 0.91 for PS, NSE = 0.57 for SAR and NSE = 0.94 for Cl) models had quite a close performance in estimation of TDS, PS, SAR, and Cl parameters and higher than the other two classical machine learning methods. As a result, the CNN model can be considered the best performing model in estimating all quality parameters due to the highest NSE and lowest RMSE values. In addition, the Taylor diagram showed that the values estimated using the CNN model had the highest correlation with the measured data. It was determined that the model with the lowest uncertainty based on the PICP statistics was DNN, followed by the CNN model. However, the CNN model has predicted outliers more accurately. Present findings proved that deep learning models could offer efficient tools for predicting irrigation water quality parameters.

Robust machine learning algorithms for predicting coastal water quality index

Coastal water quality assessment is an essential task to keep "good water quality" status for living organisms in coastal ecosystems. The Water quality index (WQI) is a widely used tool to assess water quality but this technique has received much criticism due to the model's reliability and inconsistence. The present study used a recently developed improved WQI model for calculating coastal WQIs in Cork Harbour. The aim of the research is to determine the most reliable and robust machine learning (ML) algorithm(s) to anticipate WQIs at each monitoring point instead of repeatedly employing SI and weight values in order to reduce model uncertainty. In this study, we compared eight commonly used algorithms, including Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGB), Extra Tree (ExT), Support Vector Machine (SVM), Linear Regression (LR), and Gaussian Naïve Bayes (GNB). For the purposes of developing the prediction models, the dataset was divided into two groups: training (70%) and testing (30%), whereas the models were validated using the 10-fold cross-validation method. In order to evaluate the models' performance, the RMSE, MSE, MAE, R², and PREI metrics were used in this study. The tree-based DT (RMSE = 0.0, MSE = 0.0, MAE = 0.0, R² = 1.0 and PERI = 0.0) and the ExT (RMSE = 0.0, MSE = 0.0, MAE = 0.0, R² = 1.0 and PERI = 0.0) and ensemble tree-based XGB (RMSE = 0.0, MSE = 0.0, MAE = 0.0, R² = 1.0 and PERI = +0.16 to -0.17) and RF (RMSE = 2.0, MSE = 3.80, MAE = 1.10, R² = 0.98, PERI = +3.52 to -25.38) models outperformed other models. The results of model performance and PREI indicate that the DT, ExT, and GXB models could be effective, robust and significantly reduce model uncertainty in predicting WQIs. The findings of this study are also useful for reducing model uncertainty and optimizing the WQM-WQI model architecture for predicting WQI values.

Aziz NA, Alelyani S, Mohana M, Hosain M. Improving Water Quality Index Prediction Using Regression Learning Models

Rivers are the main sources of freshwater supply for the world population. However, many economic activities contribute to river water pollution. River water quality can be monitored using various parameters, such as the pH level, dissolved oxygen, total suspended solids, and the chemical properties. Analyzing the trend and pattern of these parameters enables the prediction of the water quality so that proactive measures can be made by relevant authorities to prevent water pollution and predict the effectiveness of water restoration measures. Machine learning regression algorithms can be applied for this purpose. Here, eight machine learning regression techniques, including decision tree regression, linear regression, ridge, Lasso, support vector regression, random forest regression, extra tree regression, and the artificial neural network, are applied for the purpose of water quality index prediction. Historical data from Indian rivers are adopted for this study. The data refer to six water parameters. Twelve other features are then derived from the original six parameters. The performances of the models using different algorithms and sets of features are compared. The derived water quality rating scale features are identified to contribute toward the development of better regression models, while the linear regression and ridge offer the best performance. The best mean square error achieved is 0 and the correlation coefficient is 1.

Random forest-based modeling of stream nutrients at national level in a data-scarce region

Nutrient runoff from agricultural production is one of the main causes of water quality deterioration in river systems and coastal waters. Water quality modeling can be used for gaining insight into water quality issues in order to implement effective mitigation efforts. Process-based nutrient models are very complex, requiring a lot of input parameters and computationally expensive calibration. Recently, ML approaches have shown to achieve an accuracy comparable to the process-based models and even outperform them when describing nonlinear relationships. We used observations from 242 Estonian catchments, amounting to 469 yearly TN and 470 TP measurements covering the period 2016-2020 to train random forest (RF) models for predicting annual N and P concentrations. We used a total of 82 predictor variables, including land cover, soil, climate and topography parameters and applied a feature selection strategy to reduce the number of dependent features in the models. The SHAP method was used for deriving the most relevant predictors. The performance of our models is comparable to previous process-based models used in the Baltic region with the TN and TP model having an R² score of 0.83 and 0.52, respectively. However, as input data used in our models is easier to obtain, the models offer superior applicability in areas, where data availability is insufficient for process-based approaches. Therefore, the models enable to give a robust estimation for nutrient losses at national level and allows to capture the spatial variability of the nutrient runoff which in turn enables to provide decision-making support for regional water management plans.

Estimating the incubated river water quality indicator based on machine learning and deep learning paradigms: BOD5 Prediction

As an indicator measured by incubating organic material from water samples in rivers, the most typical characteristic of water quality items is biochemical oxygen demand (BOD₅) concentration, which is a stream pollutant with an extreme circumstance of organic loading and controlling aquatic behavior in the eco-environment. Leading monitoring approaches including machine leaning and deep learning have been evolved for a correct, trustworthy, and low-cost prediction of BOD₅ concentration. The addressed research investigated the efficiency of three standalone models including machine learning (extreme learning machine (ELM) and support vector regression (SVR)) and deep learning (deep echo state network (Deep ESN)). In addition, the novel double-stage synthesis models (wavelet-extreme learning machine (Wavelet-ELM), wavelet-support vector regression (Wavelet-SVR), and wavelet-deep echo state network (Wavelet-Deep ESN)) were developed by integrating wavelet transformation (WT) with the different standalone models. Five input associations were supplied for evaluating standalone and double-stage synthesis models by determining diverse water quantity and quality items. The proposed models were assessed using the coefficient of determination (R²), Nash-Sutcliffe (NS) efficiency, and root mean square error (RMSE). The significance of addressed research can be found from the overall outcomes that the predictive accuracy of double-stage synthesis models were not always superior to that of standalone models. Overall results showed that the SVR with 3^th distribution (NS = 0.915) and the Wavelet-SVR with 4^th distribution (NS = 0.915) demonstrated more correct outcomes for predicting BOD₅ concentration compared to alternative models at Hwangji station, and the Wavelet-SVR with 4^th distribution (NS = 0.917) was judged to be the most superior model at Toilchun station. In most cases for predicting BOD₅ concentration, the novel double-stage synthesis models can be utilized for efficient and organized data administration and regulation of water pollutants on both stations, South Korea.

Groundwater Quality: The Application of Artificial Intelligence

Humans and all other living things depend on having access to clean water, as it is an indispensable essential resource. Therefore, the development of a model that can predict water quality conditions in the future will have substantial societal and economic value. This can be accomplished by using a model that can predict future water quality circumstances. In this study, we employed a sophisticated artificial neural network (ANN) model. This study intends to develop a hybrid model of single exponential smoothing (SES) with bidirectional long short-term memory (BiLSTM) and an adaptive neurofuzzy inference system (ANFIS) to predict water quality (WQ) in different groundwater in the Al-Baha region of Saudi Arabia. Single exponential smoothing (SES) was employed as a preprocessing method to adjust the weight of the dataset, and the output from SES was processed using the BiLSTM and ANFIS models for predicting water quality. The data were randomly divided into two phases, training (70%) and testing (30%). Efficiency statistics were used to evaluate the SES-BiLSTM and SES-ANFIS models' prediction abilities. The results showed that while both the SES-BiLSTM and SES-ANFIS models performed well in predicting the water quality index (WQI), the SES-BiLSTM model performed best with accuracy (R = 99.95% and RMSE = 0.00910) at the testing phase, where the performance of the SES-ANFIS model was R = 99.95% and RMSE = 2.2941 × 100-07. The findings support the idea that the SES-BilSTM and SES-ANFIS models can be used to predict the WQI with high accuracy, which will help to enhance WQ. The results demonstrated that the SES-BiLSTM and SES-ANFIS models' forecasts are accurate and that both seasons' performances are consistent. Similar investigations of groundwater quality prediction for drinking purposes should benefit from the proposed SES-BiLSTM and SES-ANFIS models. Consequently, the results demonstrate that the proposed SES-BiLSTM and SES-ANFIS models are useful tools for predicting whether the groundwater in Al-Baha city is suitable for drinking and irrigation purposes.

Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends

This paper reviews the current GeoAI and machine learning applications in hydrological and hydraulic modeling, hydrological optimization problems, water quality modeling, and fluvial geomorphic and morphodynamic mapping. GeoAI effectively harnesses the vast amount of spatial and non-spatial data collected with the new automatic technologies. The fast development of GeoAI provides multiple methods and techniques, although it also makes comparisons between different methods challenging. Overall, selecting a particular GeoAI method depends on the application’s objective, data availability, and user expertise. GeoAI has shown advantages in non-linear modeling, computational efficiency, integration of multiple data sources, high accurate prediction capability, and the unraveling of new hydrological patterns and processes. A major drawback in most GeoAI models is the adequate model setting and low physical interpretability, explainability, and model generalization. The most recent research on hydrological GeoAI has focused on integrating the physical-based models’ principles with the GeoAI methods and on the progress towards autonomous prediction and forecasting systems.

Water quality index modeling using random forest and improved SMO algorithm for support vector machine in Saf-Saf river basin

The water quality index is one of the prominent general indicators to assess and classify surface water quality, which plays a critical role in river water resources practices. This research constructs a hybrid artificial intelligence model namely sequential minimal optimization-support vector machine (SMO-SVM) along with random forest (RF) as a benchmark model for predicting water quality values at the Wadi Saf-Saf river basin in Algeria. The fifteen input water quality datasets such as biochemical oxygen demand (BOD), oxygen saturation (OS), the potential for hydrogen (pH), chemical oxygen demand (COD), chloride (Cl^-), dissolved oxygen (DO), electrical conductivity (EC), total dissolved solids (TDS), nitrate-nitrogen (NO₃-N), nitrite-nitrogen (NO₂-N), phosphate (PO₄^3-), ammonium (NH₄⁺), temperature (T), turbidity (NTU), and suspended solids (SS) were employed for constructing the predictive models. Different input data combinations are evaluated in terms of predictive performance, using a set of statistical metrics and graphical representation. Results show that less than 40% of samples were observed to be poor quality water during the dry season in downstream northeastern part of the basin. The findings also show that the RF model mostly generates more precise water quality index predictions than the SMO-SVM model for both training and testing stages. Although thirteen input parameters attain the optimal predictive performance (R² testing = 0.82, RMSE testing = 5.17), a couple of five input parameters, e.g., only pH, EC, TDS, T, and saturation, gives the second optimal predictive precision (R² test = 0.81, RMSE testing = 5.55). The sensitivity analysis results indicate a greater sensitivity by the all input variables chosen except NO₂^- of the predictive outcomes to the earlier influencing water quality parameters. Overall, the RF model reveals an improvement on earlier tools for predicting water quality index, according to predictive performance and reducing in the number of input variables.

Water quality prediction in sea cucumber farming based on a GRU neural network optimized by an improved whale optimization algorithm

Sea cucumber farming is an important part of China's aquaculture industry, and sea cucumbers have higher requirements for aquaculture water quality. This article proposes a sea cucumber aquaculture water quality prediction model that uses an improved whale optimization algorithm to optimize the gated recurrent unit neural network(IWOA-GRU), which provides a reference for the water quality control in the sea cucumber growth environment. This model first applies variational mode decomposition (VMD) and the wavelet threshold joint denoising method to remove mixed noise in water quality time series. Then, by optimizing the convergence factor, the convergence speed and global optimization ability of the whale optimization algorithm are strengthened. Finally, the improved whale optimization algorithm is used to construct a GRU prediction model based on optimal network weights and thresholds to predict sea cucumber farming water quality. The model was trained and tested using three water quality indices (dissolved oxygen, temperature and salinity) of sea cucumber culture waters in Shandong Peninsula, China, and compared with prediction models such as support vector regression (SVR), random forest (RF), convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory neural network (LSTM). Experimental results show that the prediction accuracy and generalization performance of this model are better than those of the other compared models.

Hybrid Machine Learning Models for Soil Saturated Conductivity Prediction

The hydraulic conductivity of saturated soil is a crucial parameter in the study of any engineering problem concerning groundwater. Hydraulic conductivity mainly depends on particle size distribution, soil compaction, and properties that influence aggregation and water retention. Generally, finding simple and accurate analytical equations between the hydraulic conductivity of soil and the characteristics on which it depends is a very hard task. Machine learning algorithms can provide excellent tools for tackling highly nonlinear regression problems. Additionally, hybrid models resulting from the combination of multiple machine learning algorithms can further improve the accuracy of predictions. Five different models were built to predict saturated hydraulic conductivity using a dataset extracted from the Soil Water Infiltration Global database. The models were based on different predictors. Seven variants of each model were compared, replacing the implemented algorithm. Three variants were based on individual models, while four variants were based on hybrid models. The employed individual machine learning algorithms were Multilayer Perceptron, Random Forest, and Support Vector Regression. The model based on the largest number of predictors led to the most accurate predictions. In addition, across all models, hybrid variants based on all three algorithms and hybridized variants of Random Forest and Support Vector Regression proved to be the most accurate (R2 values up to 0.829). However, all variants showed a tendency to overestimate conductivity in soils where it is very low.

Using Machine Learning Models for Predicting the Water Quality Index in the La Buong River, Vietnam

For effective management of water quantity and quality, it is absolutely essential to estimate the pollution level of the existing surface water. This case study aims to evaluate the performance of twelve machine learning (ML) models, including five boosting-based algorithms (adaptive boosting, gradient boosting, histogram-based gradient boosting, light gradient boosting, and extreme gradient boosting), three decision tree-based algorithms (decision tree, extra trees, and random forest), and four ANN-based algorithms (multilayer perceptron, radial basis function, deep feed-forward neural network, and convolutional neural network), in estimating the surface water quality of the La Buong River in Vietnam. Water quality data at four monitoring stations alongside the La Buong River for the period 2010–2017 were utilized to calculate the water quality index (WQI). Prediction performance of the ML models was evaluated by using two efficiency statistics (i.e., R2 and RMSE). The results indicated that all twelve ML models have good performance in predicting the WQI but that extreme gradient boosting (XGBoost) has the best performance with the highest accuracy (R2 = 0.989 and RMSE = 0.107). The findings strengthen the argument that ML models, especially XGBoost, may be employed for WQI prediction with a high level of accuracy, which will further improve water quality management.

Prediction of 5-day biochemical oxygen demand in the Buriganga River of Bangladesh using novel hybrid machine learning algorithms

Biochemical oxygen demand (BOD) is one of the most important variables indicating stream pollution with a severe condition of organic loading and maintaining aquatic life in ecosystems. Advanced monitoring techniques such as machine learning (ML) methods have been developed for an accurate, reliable, and cost-effective prediction of BOD. This study investigated the effectiveness of four stand-alone ML algorithms, namely, artificial neural network (ANN), support vector machine (SVM), random forest (RF), and gradient boosting machine (GBM), and six novel hybrid algorithms, namely, RF-SVM, ANN-SVM, GBM-SVM, RF-ANN, GBM-ANN, and RF-GBM, in predicting BOD of the Buriganga river system of Bangladesh. The feature importance analysis of RF algorithm indicated that chemical oxygen demand (COD), total dissolved solids (TDS), conductivity, total solids (TS), suspended solids (SS), and turbidity are the most influential parameters for predicting BOD₅ . The significance of this study is the application of the novel hybrid models that resulted in higher prediction success; RF-SVM with the highest R² value (0.908). The employed novel hybrid ML models can be particularly useful for efficient and systematic data management, water pollution control, and prevention in developing countries such as Bangladesh. PRACTITIONER POINTS: Investigated the efficiency of four stand-alone and six novel hybrid ML models for predicting BOD in a river of Bangladesh. The significance of this study is the application of the six novel hybrid models that resulted in higher prediction success. The best three prediction models were RF-SVM, ANN-SVM, and GBM-SVM with a prediction success of 91%, 89.6%, and 88.8% respectively. ML models indicated COD, conductivity, TDS, TS, SS, and turbidity as the most influential variables for predicting BOD. The novel hybrid models can be useful for developing countries for efficient systematic data management, pollution control, and prevention strategies.