Modelling and Prediction of Water Quality by Using Artificial Intelligence

Interpreting optimised data-driven solution with explainable artificial intelligence (XAI) for water quality assessment for better decision-making in pollution management

In Saudi Arabia, water pollution and drinking water scarcity pose a major challenge and jeopardise the achievement of sustainable development goals. The urgent need for rapid and accurate monitoring and assessment of water quality requires sophisticated, data-driven solutions for better decision-making in water management. This study aims to develop optimised data-driven models for comprehensive water quality assessment to enable informed decisions that are critical for sustainable water resources management. We used an entropy-weighted arithmetic technique to calculate the Water Quality Index (WQI), which integrates the World Health Organization (WHO) standards for various water quality parameters. Our methodology incorporated advanced machine learning (ML) models, including decision trees, random forests (RF) and correlation analyses to select features essential for identifying critical water quality parameters. We developed and optimised data-driven models such as gradient boosting machines (GBM), deep neural networks (DNN) and RF within the H2O API framework to ensure efficient data processing and handling. Interpretation of these models was achieved through a three-pronged explainable artificial intelligence (XAI) approach: model diagnosis with residual analysis, model parts with permutation-based feature importance and model profiling with partial dependence plots (PDP), accumulated local effects (ALE) plots and individual conditional expectation (ICE) plots. The quantitative results revealed insightful findings: fluoride and residual chlorine had the highest and lowest entropy weights, respectively, indicating their differential effects on water quality. Over 35% of the water samples were categorised as 'unsuitable' for consumption, highlighting the urgency of taking action to improve water quality. Amongst the optimised models, the Random Forest (model 79) and the Deep Neural Network (model 81) proved to be the most effective and showed robust predictive abilities with R2 values of 0.96 and 0.97 respectively for testing dataset. Model profiling as XAI highlighted the significant influence of key parameters such as nitrate, total hardness and pH on WQI predictions. These findings enable targeted water quality improvement measures that are in line with sustainable water management goals. Therefore, our study demonstrates the potential of advanced, data-driven methods to revolutionise water quality assessment in Saudi Arabia. By providing a more nuanced understanding of water quality dynamics and enabling effective decision-making, these models contribute significantly to the sustainable management of valuable water resources.

Artificial intelligence and machine learning algorithms in the detection of heavy metals in water and wastewater: Methodological and ethical challenges

Heavy metals (HMs) enter waterbodies through various means, which, when exceeding a threshold limit, cause toxic effects both on the environment and in humans upon entering their systems. Recent times have seen an increase in such HM influx incident rates. This requires an instant response in this regard to review the challenges in the available classical methods for HM detection and removal. As well as provide an opportunity to explore the applications of artificial intelligence (AI) and machine learning (ML) for the identification and further redemption of water and wastewater from the HMs. This review of research focuses on such applications in conjunction with the available in-silico models producing worldwide data for HM levels. Furthermore, the effect of HMs on various disease progressions has been provided, along with a brief account of prediction models analysing the health impact of HM intoxication. Also discussing the ethical and other challenges associated with the use of AI and ML in this field is the futuristic approach intended to follow, opening a wide scope of possibilities for improvement in wastewater treatment methodologies.

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).

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.

Groundwater quality index development using the ANN model of Delhi Metropolitan City, India

Groundwater is widely recognized as a vital source of fresh drinking water worldwide. However, the rapid, unregulated population growth and increased industrialization, coupled with a rise in human activities, have significantly harmed the quality of groundwater. Changes in the local topography and drainage systems in an area have negative impacts on both the quality and quantity of groundwater. This underscores the critical need to assess the susceptibility of groundwater to pollution and implement measures to mitigate these risks. The water quality index (WQI) is an approach that simulates the water quality at peculiar locations for a particular period of time. The artificial neural network (ANN) model approach is such an idealistic methodology that can be utilized for WQI development and provides better results for specific locations in optimum time. Therefore, the goal of the current study is to provide a unique way for using artificial neural networks (ANN) to characterize the groundwater quality of Delhi Metropolitan City, India. In order to make the water fit for residential and drinking use, the research also pinpoints the geographical variability and spots where the contaminated region has to be sufficiently cleaned. A minimum WQI of 41.51 was obtained at the Jagatpur location while a maximum value of 779.01 was at the Peeragarhi location. During the training phase, the results obtained using the ANN model were highly favorable, demonstrating a strong association with an R-value of 98.10%, thus highlighting the program's exceptional efficiency. However, in accordance with the correlation regression findings, the prediction outcomes of the ANN model in testing are observed to be an R-value of 99.99-100%. This study confirms the promise and advantages of employing advanced artificial intelligence in managing groundwater quality in the studied area.

Application of Artificial Intelligence in the Management of Drinking Water: A Narrative Review

Waterborne illnesses are a significant concern worldwide. The management of water resources can be facilitated by artificial intelligence (AI) with the help of data analytics, regression models, and algorithms. Achieving the Sustainable Development Goals (SDGs) of the 2030 Agenda for Sustainable Development of the United Nations depends on understanding, communicating, and measuring the value of water and incorporating it into decision-making. Various barriers are used from the source to the consumer to prevent microbiological contamination of drinking water sources or reduce contamination to levels safe for human health. Infrastructure development and capacity-building policies should be integrated with guidelines on applying AI to problems relating to water to ensure good development outcomes. Communities can live healthily with such technology if they can provide clean, economical, and sustainable water to the ecosystem as a whole. Quick and accurate identification of waterborne pathogens in drinking and recreational water sources is essential for treating and controlling the spread of water-related diseases, especially in resource-constrained situations. To ensure successful development outcomes, policies on infrastructure development and capacity building should be combined with those on applying AI to water-related problems. The primary focus of this study is the use of AI in managing drinking water and preventing waterborne illness.

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.

Artificial intelligence models for methylene blue removal using functionalized carbon nanotubes

This study aims to assess the practicality of utilizing artificial intelligence (AI) to replicate the adsorption capability of functionalized carbon nanotubes (CNTs) in the context of methylene blue (MB) removal. The process of generating the carbon nanotubes involved the pyrolysis of acetylene under conditions that were determined to be optimal. These conditions included a reaction temperature of 550 °C, a reaction time of 37.3 min, and a gas ratio (H2/C2H2) of 1.0. The experimental data pertaining to MB adsorption on CNTs was found to be extremely well-suited to the Pseudo-second-order model, as evidenced by an R2 value of 0.998, an X2 value of 5.75, a qe value of 163.93 (mg/g), and a K2 value of 6.34 × 10-4 (g/mg min).The MB adsorption system exhibited the best agreement with the Langmuir model, yielding an R2 of 0.989, RL value of 0.031, qm value of 250.0 mg/g. The results of AI modelling demonstrated a remarkable performance using a recurrent neural network, achieving with the highest correlation coefficient of R2 = 0.9471. Additionally, the feed-forward neural network yielded a correlation coefficient of R2 = 0.9658. The modeling results hold promise for accurately predicting the adsorption capacity of CNTs, which can potentially enhance their efficiency in removing methylene blue from wastewater.

Drinking Water Quality Assessment Using a Fuzzy Inference System Method: A Case Study of Rome (Italy)

Drinking water quality assessment is a major issue today, as it is crucial to supply safe drinking water to ensure the well-being of society. Predicting drinking water quality helps strengthen water management and fight water pollution; technologies and practices for drinking water quality assessment are continuously improving; artificial intelligence methods prove their efficiency in this domain. This research effort seeks a hierarchical fuzzy model for predicting drinking water quality in Rome (Italy). The Mamdani fuzzy inference system is applied with different defuzzification methods. The proposed model includes three fuzzy intermediate models and one fuzzy final model. Each model consists of three input parameters and 27 fuzzy rules. A water quality assessment model is developed with a dataset that considers nine parameters (alkalinity, hardness, pH, Ca, Mg, fluoride, sulphate, nitrates, and iron). These nine parameters of drinking water are anticipated to be within the acceptable limits set to protect human health. Fuzzy-logic-based methods have been demonstrated to be appropriate to address uncertainty and subjectivity in drinking water quality assessment; they are an effective method for managing complicated, uncertain water systems and predicting drinking water quality. The proposed method can provide an effective solution for complex systems; this method can be modified easily to improve performance.

Assessing and forecasting water quality in the Danube River by using neural network approaches

The health and quality of the Danube River ecosystems is strongly affected by the nutrients loads (N and P), degree of contamination with hazardous substances or with oxygen depleting substances, microbiological contamination and changes in river flow patterns and sediment transport regimes. Water quality index (WQI) is an important dynamic attribute in the characterization of the Danube River ecosystems health and quality. The WQ index scores do not reflect the actual condition of water quality. We proposed a new forecast scheme for water quality based on the following qualitative classes very good (0-25), good (26-50), poor (51-75), very poor (76-100) and extremely polluted/non-potable (>100). Water quality forecasting by using Artificial Intelligence (AI) is a meaningful method of protecting public health because of its possibility to provide early warning regarding harmful water pollutants. The main objective of the present study is to forecast the WQI time series data based on water physical, chemical and flow status parameters and associated WQ index scores. The Cascade-forward network (CFN) models, along with the Radial Basis Function Network (RBF) as a benchmark model, were developed using data from 2011 to 2017 and WQI forecasts were produced for the period 2018-2019 at all sites. The nineteen input water quality features represent the initial dataset. Moreover, the Random Forest (RF) algorithm refines the initial dataset by selecting eight features considered the most relevant. Both datasets are employed for constructing the predictive models. According to the results of appraisal, the CFN models produced better outcomes (MSE = 0.083/0,319 and R-value 0.940/0.911 in quarter I/quarter IV) than the RBF models. In addition, results show that both the CFN and RBF models could be effective for predicting time series data for water quality when the eight most relevant features are used as input variables. Also, the CFNs provide the most accurate short-term forecasting curves which reproduce the WQI for the first and fourth quarters (the cold season). The second and third quarters presented a slightly lower accuracy. The reported results clearly demonstrate that CFNs successfully forecast the short-term WQI as they may learn historic patterns and determine the nonlinear relationships between the input and output variables.

Water treatment and artificial intelligence techniques: a systematic literature review research

As clean water can be considered among the essentials of human life, there is always a requirement to seek its foremost and high quality. Water primarily becomes polluted due to organic as well as inorganic pollutants, including nutrients, heavy metals, and constant contamination with organic materials. Predicting the quality of water accurately is essential for its better management along with controlling pollution. With stricter laws regarding water treatment to remove organic and biologic materials along with different pollutants, looking for novel technologic procedures will be necessary for improved control of the treatment processes by water utilities. Linear regression-based models with relative simplicity considering water prediction have been typically used as available statistical models. Nevertheless, in a majority of real problems, particularly those associated with modeling of water quality, non-linear patterns will be observed, requiring non-linear models to address them. Thus, artificial intelligence (AI) can be a good candidate in modeling and optimizing the elimination of pollutants from water in empirical settings with the ability to generate ideal operational variables, due to its recent considerable advancements. Management and operation of water treatment procedures are supported technically by these technologies, leading to higher efficiency compared to sole dependence on human operations. Thus, establishing predictive models for water quality and subsequently, more efficient management of water resources would be critically important, serving as a strong tool. A systematic review methodology has been employed in the present work to investigate the previous studies over the time interval of 2010-2020, while analyzing and synthesizing the literature, particularly regarding AI application in water treatment. A total number of 92 articles had addressed the topic under study using AI. Based on the conclusions, the application of AI can obviously facilitate operations, process automation, and management of water resources in significantly volatile contexts.

Artificial intelligence and machine learning-based monitoring and design of biological wastewater treatment systems

Artificial intelligence (AI) and machine learning (ML) are currently used in several areas. The applications of AI and ML based models are also reported for monitoring and design of biological wastewater treatment systems (WWTS). The available information is reviewed and presented in terms of bibliometric analysis, model's description, specific applications, and major findings for investigated WWTS. Among the applied models, artificial neural network (ANN), fuzzy logic (FL) algorithms, random forest (RF), and long short-term memory (LSTM) were predominantly used in the biological wastewater treatment. These models are tested by predictive control of effluent parameters such as biological oxygen demand (BOD), chemical oxygen demand (COD), nutrient parameters, solids, and metallic substances. Following model performance indicators were mainly used for the accuracy analysis in most of the studies: root mean squared error (RMSE), mean square error (MSE), and determination coefficient (DC). Besides, outcomes of various models are also summarized in this study.

Artificial ecosystem optimization with Deep Learning Enabled Water Quality Prediction and Classification model

The majority of what is needed to maintain life is found in the approximately 70 percent of the earth's surface that is composed of water. Water quality has been deteriorating at an alarming rate as a direct result of rapid industrialization and urbanisation, which has led to a rise in the prevalence of serious diseases. In the past, determining the quality of water was typically accomplished by employing labor-intensive, time-consuming, and statistically pricey laboratory investigations, which renders the prevalent concept of real-time monitoring meaningless. The worrisome effect of poor water quality mandates the necessity of an alternative model that is both rapid and economical to implement. There has been a lot of talk about using artificial intelligence to forecast and model water quality as a means of preventing and reducing water pollution. An artificial ecosystem optimization with Deep Learning Enabled Water Quality Prediction and Classification (AEODL-WQPC) model is presented in this paper. The primary objectives of the AEODL-WQPC model that is being given are the prediction and categorization of different levels of water quality. As a first processing step, the data normalization technique is used to the provided AEODL-WQPC model so that this goal can be achieved. In addition to this, an optimal stacked bidirectional gated recurrent unit (OSBiGRU) model is used to forecast the water quality index (WQI), and the Adam optimizer is utilised in order to fine-tune the model's parameters. AEO with enhanced Elman Neural Network (AEO-IENN) model is utilised for the categorization of water quality. This model is characterized by the fact that the AEO algorithm effectively tunes the parameters associated to the ENN model. For the purposes of the experimental validation of the AEODL-WQPC model, a benchmark water quality dataset obtained from the Kaggle repository is utilised. The research that compared several models found that the AEODL-WQPC model had superior results to more recent state of the art methods.

A portable spectroscopic instrument for multiplexed monitoring of acute water toxicity: Design, testing, and evaluation

The deteriorating water environment worldwide, mainly due to population explosion and uncontrolled direct disposal of harmful industrial and farming wastes, earnestly demands new approaches and accurate technologies to monitor water quality before consumption overcoming the shortcomings of the current methodologies. A spectroscopic water quality monitoring and early-warning instrument for evaluating acute water toxicity are the need of the hour. In this study, we have developed a prototype capable of the quantification of dissolved organic matter, dissolved chemicals, and suspended particulate matter in trace amounts dissolved in the water. The prototype estimates the water quality of the samples by measuring the absorbance, fluorescence, and scattering of the impurities simultaneously. The performance of the instrument was evaluated by detecting common water pollutants such as Benzopyrene, Crystal Violet, and Titanium di-oxide. The limit of detection values was found to be 0.50, 23.9, and 23.2 ppb (0.29 µM), respectively.

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.

Comparison between Regression Models, Support Vector Machine (SVM), and Artificial Neural Network (ANN) in River Water Quality Prediction

Both anthropogenic and natural sources of pollution are regionally significant. Therefore, in order to monitor and protect the quality of Langat River from deterioration, we use Artificial Intelligence (AI) to model the river water quality. This study has applied several machine learning models (two support vector machines (SVMs), six regression models, and artificial neural network (ANN)) to predict total suspended solids (TSS), total solids (TS), and dissolved solids (DS)) in Langat River, Malaysia. All of the models have been assessed using root mean square error (RMSE), mean square error (MSE) as well as the determination of coefficient (R2). Based on the model performance metrics, the ANN model outperformed all models, while the GPR and SVM models exhibited the characteristic of over-fitting. The remaining machine learning models exhibited fair to poor performances. Although there are a few researches conducted to predict TDS using ANN, however, there are less to no research conducted to predict TS and TSS in Langat River. Therefore, this is the first study to evaluate the water quality (TSS, TS, and DS) of Langat River using the aforementioned models (especially SVM and the six regression models).

A Review of Hybrid Soft Computing and Data Pre-Processing Techniques to Forecast Freshwater Quality’s Parameters: Current Trends and Future Directions

Water quality has a significant influence on human health. As a result, water quality parameter modelling is one of the most challenging problems in the water sector. Therefore, the major factor in choosing an appropriate prediction model is accuracy. This research aims to analyse hybrid techniques and pre-processing data methods in freshwater quality modelling and forecasting. Hybrid approaches have generally been seen as a potential way of improving the accuracy of water quality modelling and forecasting compared with individual models. Consequently, recent studies have focused on using hybrid models to enhance forecasting accuracy. The modelling of dissolved oxygen is receiving more attention. From a review of relevant articles, it is clear that hybrid techniques are viable and precise methods for water quality prediction. Additionally, this paper presents future research directions to help researchers predict freshwater quality variables.

Using Artificial Intelligence for Space Challenges: A Survey

Artificial intelligence is applied to many fields and contributes to many important applications and research areas, such as intelligent data processing, natural language processing, autonomous vehicles, and robots. The adoption of artificial intelligence in several fields has been the subject of many research papers. Still, recently, the space sector is a field where artificial intelligence is receiving significant attention. This paper aims to survey the most relevant problems in the field of space applications solved by artificial intelligence techniques. We focus on applications related to mission design, space exploration, and Earth observation, and we provide a taxonomy of the current challenges. Moreover, we present and discuss current solutions proposed for each challenge to allow researchers to identify and compare the state of the art in this context.

Vision Robot Path Control Based on Artificial Intelligence Image Classification and Sustainable Ultrasonic Signal Transformation Technology

The unsupervised algorithm of artificial intelligence (AI), named ART (Adaptive Resonance Theory), is used to first roughly classify an image, that is, after the image is processed by the edge filtering technology, the image window is divided into 25 square areas of 5 rows and 5 columns, and then, according to the location of the edge of the image, it determines whether the robot should go straight (represented by S), turn around (represented by A), stop (T), turn left (represented by L), or turn right (represented by R). Then, after sustainable ultrasonic signal acquisition and transformation into digital signals are completed, the sustainable supervised neural network named SGAFNN (Supervised Gaussian adaptive fuzzy neural network) will perform an optimal path control that can accurately control the traveling speed and turning of the robot to avoid hitting walls or obstacles. Based on the above, this paper proposes the use of the ART operation after image processing to judge the rough direction, followed by the use of the ultrasonic signal to carry out the sustainable development of artificial intelligence and to carry out accurate speed and direction SGAFNN control to avoid obstacles. After simulation and practical evaluations, the proposed method is proved to be feasible and to exhibit good performance.

Modeling Multistep Ahead Dissolved Oxygen Concentration Using Improved Support Vector Machines by a Hybrid Metaheuristic Algorithm

Dissolved oxygen (DO) concentration is an important water-quality parameter, and its estimation is very important for aquatic ecosystems, drinking water resources, and agro-industrial activities. In the presented study, a new support vector machine (SVM) method, which is improved by hybrid firefly algorithm–particle swarm optimization (FFAPSO), is proposed for the accurate estimation of the DO. Daily pH, temperature (T), electrical conductivity (EC), river discharge (Q) and DO data from Fountain Creek near Fountain, the United States, were used for the model development. Various combinations of pH, T, EC, and Q were used as inputs to the models to estimate the DO. The outcomes of the proposed SVM–FFAPSO model were compared with the SVM–PSO, SVM–FFA, and standalone SVM with respect to the root mean square errors (RMSE), the mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE), and determination coefficient (R2), and graphical methods, such as scatterplots, and Taylor and violin charts. The SVM–FFAPSO showed a superior performance to the other methods in the estimation of the DO. The best model of each method was also assessed in multistep-ahead (from 1- to 7-day ahead) DO, and the superiority of the proposed method was observed from the comparison. The general outcomes recommend the use of SVM–FFAPSO in DO modeling, and this method can be useful for decision-makers in urban water planning and management.

Applications of various data-driven models for the prediction of groundwater quality index in the Akot basin, Maharashtra, India

Data-driven models are important to predict groundwater quality which is controlling human health. The water quality index (WQI) has been developed based on the physicochemical parameters of water samples. In this area, water quality is medium to poor and is found in saline zones; very high pH ranges are directly affected on the water quality in this study area. Conventional WQI computation demands more time and is often observed with enormous errors during the calculation of sub-indices. In the present work, four standalone methods such as additive regression (AR), M5P tree model (M5P), random subspace (RSS), and support vector machine (SVM) were employed to predict WQI based on variable elimination technique. The groundwater samples were collected from the Akot basin area, located in the Akola district, Maharashtra, in India. A total of nine different input combinations were developed in this study. The datasets were demarcated into two classes (ratio 80:20) for model construction (training dataset) and model verification (testing dataset) using a fivefold cross-validation approach. The models were assessed using statistical and graphical appraisal metrics. The best input combinations varied among the model, generally, the optimal input variables (EC, pH, TDS, Ca, Mg, and Cl) during the training and validation stages. Results show that AR outperformed the other data-driven models (R2 = 0.9993, MAE = 0.5243, RMSE = 0.0.6356, %RAE = 3.8449, and RRSE% = 3.9925). The AR is proposed as an ideal model with satisfactory results due to enhanced prediction precision with the minimum number of input parameters and can thus act as the reliable and precise method in the prediction of WQI at the Akot basin.

Water quality prediction based on Naïve Bayes algorithm

In the fast-changing world with increased water demand, water pollution, environmental problems, and related data, information on water quality and suitability for any purpose should be prompt and reliable. Traditional approaches often fail in the attempt to predict water quality classes and new ones are needed to handle a large amount or missing data to predict water quality in real time. One of such approaches is machine-learning (ML) based prediction. This paper presents the results of the application of the Naïve Bayes, a widely used ML method, in creating the prediction model. The proposed model is based on nine water quality parameters: temperature, pH value, electrical conductivity, oxygen saturation, biological oxygen demand, suspended solids, nitrogen oxides, orthophosphates, and ammonium. It is created in Netica software and tested and verified using data covering the period 2013-2019 from five locations in Vojvodina Province, Serbia. Forty-eight samples were used to train the model. Once trained, the Naïve Bayes model correctly predicted the class of water sample in 64 out of 68 cases, including cases with missing data. This recommends it as a trustful tool in the transition from traditional to digital water management.

Evaluating the Pressure and Loss Behavior in Water Pipes Using Smart Mathematical Modelling

Due to the constant need to enhance water supply sources, water operators are searching for solutions to maintain water quality through leakage protection. The capability to monitor the day-to-day water supply management is one of the most significant operational challenges for water companies. These companies are looking for ways to predict how to improve their supply operations in order to remain competitive, given the rising demand. This work focuses on the mathematical modeling of water flow and losses through leak openings in the smart pipe system. The research introduces smart mathematical models that water companies may use to predict water flow, losses, and performance, thereby allowing issues and challenges to be effectively managed. So far, most of the modeling work in water operations has been based on empirical data rather than mathematically described process relationships, which is addressed in this study. Moreover, partial submersion had a power relationship, but a total immersion was more likely to have a linear power relationship. It was discovered in the experiment that the laminar flows had Reynolds numbers smaller than 2000. However, when testing with transitional flows, Reynolds numbers were in the range of 2000 to 4000. Furthermore, tests with turbulent flow revealed that the Reynolds number was more than 4000. Consequently, the main loss in a 30 mm diameter pipe was 0.25 m, whereas it was 0.01 m in a 20 mm diameter pipe. However, the fitting pipe had a minor loss of 0.005 m, whereas the bending pipe had a loss of 0.015 m. Consequently, mathematical models are required to describe, forecast, and regulate the complex relationships between water flow and losses, which is a concept that water supply companies are familiar with. Therefore, these models can assist in designing and operating water processes, allowing for improved day-to-day performance management.

Prediction of Aquatic Ecosystem Health Indices through Machine Learning Models Using the WGAN-Based Data Augmentation Method

Changes in hydrological characteristics and increases in various pollutant loadings due to rapid climate change and urbanization have a significant impact on the deterioration of aquatic ecosystem health (AEH). Therefore, it is important to effectively evaluate the AEH in advance and establish appropriate strategic plans. Recently, machine learning (ML) models have been widely used to solve hydrological and environmental problems in various fields. However, in general, collecting sufficient data for ML training is time-consuming and labor-intensive. Especially in classification problems, data imbalance can lead to erroneous prediction results of ML models. In this study, we proposed a method to solve the data imbalance problem through data augmentation based on Wasserstein Generative Adversarial Network (WGAN) and to efficiently predict the grades (from A to E grades) of AEH indices (i.e., Benthic Macroinvertebrate Index (BMI), Trophic Diatom Index (TDI), Fish Assessment Index (FAI)) through the ML models. Raw datasets for the AEH indices composed of various physicochemical factors (i.e., WT, DO, BOD5, SS, TN, TP, and Flow) and AEH grades were built and augmented through the WGAN. The performance of each ML model was evaluated through a 10-fold cross-validation (CV), and the performances of the ML models trained on the raw and WGAN-based training sets were compared and analyzed through AEH grade prediction on the test sets. The results showed that the ML models trained on the WGAN-based training set had an average F1-score for grades of each AEH index of 0.9 or greater for the test set, which was superior to the models trained on the raw training set (fewer data compared to other datasets) only. Through the above results, it was confirmed that by using the dataset augmented through WGAN, the ML model can yield better AEH grade predictive performance compared to the model trained on limited datasets; this approach reduces the effort needed for actual data collection from rivers which requires enormous time and cost. In the future, the results of this study can be used as basic data to construct big data of aquatic ecosystems, needed to efficiently evaluate and predict AEH in rivers based on the ML models.

Forecasting Water Quality Index in Groundwater Using Artificial Neural Network

Groundwater quality monitoring in the vicinity of drilling sites is crucial for the protection of water resources. Selected physicochemical parameters of waters were marked in the study. The water was collected from 19 wells located close to a shale gas extraction site. The water quality index was determined from the obtained parameters. A secondary objective of the study was to test the capacity of the artificial neural network (ANN) methods to model the water quality index in groundwater. The number of ANN input parameters was optimized and limited to seven, which was derived using a multiple regression model. Subsequently, using the stepwise regression method, models with ever fewer variables were tested. The best parameters were obtained for a network with five input neurons (electrical conductivity, pH as well as calcium, magnesium and sodium ions), in addition to five neurons in the hidden layer. The results showed that the use of the parameters is a convenient approach to modeling water quality index with satisfactory and appropriate accuracy. Artificial neural network methods exhibited the capacity to predict water quality index at the desirable level of accuracy (RMSE = 0.651258, R = 0.9992 and R2 = 0.9984). Neural network models can thus be used to directly predict the quality of groundwater, particularly in industrial areas. This proposed method, using advanced artificial intelligence, can aid in water treatment and management. The novelty of these studies is the use of the ANN network to forecast WQI groundwater in an area in eastern Poland that was not previously studied—in Lublin.

Comparative Study of Computational Models for Reducing Air Pollution through the Generation of Negative Ions

Today, air quality is one of the global concerns that governments are facing. One of the main air pollutants is the particulate matter (PM) which affects human health. This article presents the modeling of a purification system by means of negative air ions (NAIs) for air pollutant removal, using computational intelligence methods. The system uses a high-voltage booster output to ionize air molecules from stainless steel electrodes; its particle-capturing efficiency reaches up to 97%. With two devices (5 cm × 2 cm × 2.5 cm), 2 trillion negative ions are produced per second, and the particulate matter (PM 2.5) can be reduced from 999 to 0 mg/m3 in a period of approximately 5 to 7 minutes (in a 40 cm × 40 cm × 40 cm acrylic chamber). This negative ion generator is a viable and sustainable alternative to reduce polluting emissions, with beneficial effects on human health.