Artificial intelligence for the prediction of water quality index in groundwater systems

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.

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.

Neuro-fuzzy modelling of a continuous stirred tank bioreactor with ceramic membrane technology for treating petroleum refinery effluent: a case study from Assam, India

A continuous stirred tank bioreactor (CSTB) with cell recycling combined with ceramic membrane technology and inoculated with Rhodococcus opacus PD630 was employed to treat petroleum refinery wastewater for simultaneous chemical oxygen demand (COD) removal and lipid production from the retentate obtained during wastewater treatment. In the present study, the COD removal efficiency (CODRE) (%) and lipid concentration (g/L) were predicted using two artificial intelligence models, i.e., an artificial neural network (ANN) and a neuro-fuzzy neural network (NF-NN) with a network topology of 6-25-2 being the best for NF-NN. The results revealed the superiority of NF-NN over ANN in terms of determination coefficient (R2), root mean square error (RMSE), and mean absolute percentage error (MAPE). Three learning algorithms were tested with NF-NN; among them, the Bayesian regularization backpropagation (BR-BP) outperformed others. The sensitivity analysis revealed that, if solid retention time and biomass concentrations were maintained between 35 and 75 h and 3.0 g/L and 3.5 g/L, respectively, high CODRE (93%) and lipid concentration (2.8 g/L) could be obtained consistently.

Prediction of irrigation water suitability using geospatial computing approach: a case study of Agartala city, India

An increase in population expansion, urban sprawling environment, and climate change has resulted in increased food demand, water scarcity, environmental pollution, and mismanagement of water resources. Groundwater, i.e., one of the most precious and mined natural resources is used to address a variety of environmental demands. Among all, irrigation is one of the leading consumers of groundwater. Various natural heterogeneities and anthropogenic activities have impacted the groundwater quality. As a result, monitoring groundwater quality and determining its suitability are critical for the sustainable long-term management of groundwater resources. In this study, groundwater samples from 35 different sampling stations were collected and tested for various parameters associated with irrigation water quality. Hybrid MCDM (fuzzy-AHP) method was used to determine the groundwater suitability for irrigation purposes. The suitability map obtained using spatial overlay analysis was classified into low, moderate, and high irrigation water suitability zones. Along with suitability analysis, various regression-based machine learning models such as multiple linear regression (MLR), random forest (RF), and artificial neural network (ANN) were used and compared to predict irrigation water suitability. Results depicted that the ANN model with the highest R2 value of 0.990 and RMSE value near to zero (0) has outperformed all other models. The present methodology could be found useful to predict irrigation water suitability in the region where regular sampling and analysis are quite challenging.

Evolution of neural network to deep learning in prediction of air, water pollution and its Indian context

The scenario of developed and developing countries nowadays is disturbed due to modern living style which affects environment, wildlife and natural habitat. Environmental quality has become or is a subject of major concern as it is responsible for health hazard of mankind and animals. Measurements and prediction of hazardous parameters in different fields of environment is a recent research topic for safety and betterment of people as well as nature. Pollution in nature is an after-effect of civilization. To combat the damage already happened, some processes should be evolved for measurement and prediction of pollution in various fields. Researchers of all over the world are active to find out ways of predicting such hazard. In this paper, application of neural network and deep learning algorithms is chosen for air pollution and water pollution cases. The purpose of this review is to reveal how family of neural network algorithms has applied on these two pollution parameters. In this paper, importance is given on algorithm, and datasets used for air and water pollution as well as the predicted parameters have also been noted for ease of future development. One major concern of this paper is Indian context of air and water pollution research, and the research potential presents in this area using Indian dataset. Another aspect for including both air and water pollutions in one review paper is to generate an idea of artificial neural network and deep learning techniques which can be cross applicable for future purpose.

Assessment of groundwater quality in Ranchi district, Jharkhand, India, using water evaluation indices and multivariate statistics

Groundwater is the most abundant liquid freshwater on earth. Rapid urbanization in developing nations (like India) has led to increased groundwater withdrawal, adversely affecting the physicochemical characteristics. Ranchi district, Jharkhand, is a part of the smart city mission development plan of the government of India. Hence, to ensure safe and clean drinking water, it is necessary to assess groundwater quality and devise development plans. Seventeen physicochemical properties and metal(loid)s contents were analyzed to determine the groundwater quality. Various pollution indices such as water quality index (WQI), metal evaluation index (MI), heavy metal pollution index (HPI), and modified degree of contamination (mC_d) are evaluated using arithmetic weighted value index and presented in a map using Arc GIS inverse distance weighting interpolation method. Chemometric analyses such as correlation, principal component, and cluster analysis were done to identify the source and determine the pollution state. A multiple linear regression model is employed to predict the impact of heavy metal and metalloid concentration on the WQI of the region. WQI shows that groundwater quality in Khelari (100.95) and Bundu (92.52) regions are highly degraded, whereas MI and HPI suggest that Ormanjhi (MI = 53.98) and Rahe (HPI = 109.20) are highly affected by metal contamination. The mC_d suggests that Ormanjhi (97.15) has the highest degree of contamination. The contaminant sources were natural (geogenic processes) and anthropogenic (mining and industrial emissions). The high metal(loid)s concentration may soon result in groundwater quality degradation in the metal-affected regions.

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.

Assessment of Algorithm Performance on Predicting Total Dissolved Solids Using Artificial Neural Network and Multiple Linear Regression for the Groundwater Data

Estimating groundwater quality parameters through conventional methods is time-consuming through laboratory measurements for megacities. There is a need to develop models that can help decision-makers make policies for sustainable groundwater reserves. The current study compared the efficiency of multivariate linear regressions (MLR) and artificial neural network (ANN) models in the prediction of groundwater parameters for total dissolved solids (TDS) for three sub-divisions in Lahore, Pakistan. The data for this study were collected every quarter of a year for six years. ANN was applied to investigate the feasibility of feedforward, backpropagation neural networks with three training functions T-BR (Bayesian regularization backpropagation), T-LM (Levenberg–Marquardt backpropagation), and T-SCG (scaled conjugate backpropagation). Two activation functions were used to analyze the performance of algorithmic training functions, i.e., Logsig and Tanh. Input parameters of pH, electrical conductivity (EC), calcium (Ca2+), magnesium (Mg2+), chloride (Cl−), and sulfate (SO42−) was used to predict TDS as an output parameter. The computed values of TDS by ANN and MLR were in close agreement with their respective measured values. Comparative analysis of ANN and MLR showed that TDS root means square error (RMSE) for city sub-division and Pearson’s coefficient of correlation (r) for ANN and MLR were 2.9% and 0.981 and 4.5% and 0.978, respectively. Similarly, for the Farrukhabad sub-division, RMSE and r for ANN were 4.9% and 0.952, while RMSE and r for MLR were 5.5% and 0.941, respectively. For the Shahadra sub-division, RMSE was 10.8%, r was 0.869 for ANN, RMSE was 11.3%, and r was 0.860 for MLR. The results exhibited that the ANN model showed less error in results than MLR. Therefore, ANN can be employed successfully as a groundwater quality prediction tool for TDS assessment.

Potential for Artificial Intelligence (AI) and Machine Learning (ML) Applications in Biodiversity Conservation, Managing Forests, and Related Services in India

The recent advancement in data science coupled with the revolution in digital and satellite technology has improved the potential for artificial intelligence (AI) applications in the forestry and wildlife sectors. India shares 7% of global forest cover and is the 8th most biodiverse region in the world. However, rapid expansion of developmental projects, agriculture, and urban areas threaten the country’s rich biodiversity. Therefore, the adoption of new technologies like AI in Indian forests and biodiversity sectors can help in effective monitoring, management, and conservation of biodiversity and forest resources. We conducted a systematic search of literature related to the application of artificial intelligence (AI) and machine learning algorithms (ML) in the forestry sector and biodiversity conservation across globe and in India (using ISI Web of Science and Google Scholar). Additionally, we also collected data on AI-based startups and non-profits in forest and wildlife sectors to understand the growth and adoption of AI technology in biodiversity conservation, forest management, and related services. Here, we first provide a global overview of AI research and application in forestry and biodiversity conservation. Next, we discuss adoption challenges of AI technologies in the Indian forestry and biodiversity sectors. Overall, we find that adoption of AI technology in Indian forestry and biodiversity sectors has been slow compared to developed, and to other developing countries. However, improving access to big data related to forest and biodiversity, cloud computing, and digital and satellite technology can help improve adoption of AI technology in India. We hope that this synthesis will motivate forest officials, scientists, and conservationists in India to explore AI technology for biodiversity conservation and forest 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.

Scaling an Artificial Neural Network-Based Water Quality Index Model from Small to Large Catchments

Scaling models is one of the challenges for water resource planning and management, with the aim of bringing the developed models into practice by applying them to predict water quality and quantity for catchments that lack sufficient data. For this study, we evaluated artificial neural network (ANN) training algorithms to predict the water quality index in a source catchment. Then, multiple linear regression (MLR) models were developed, using the predicted water quality index of the ANN training algorithms and water quality variables, as dependent and independent variables, respectively. The most appropriate MLR model has been selected on the basis of the Akaike information criterion, sensitivity and uncertainty analyses. The performance of the MLR model was then evaluated by a variable aggregation and disaggregation approach, for upscaling and downscaling proposes, using the data from four very large- and three large-sized catchments and from eight medium-, three small- and seven very small-sized catchments, where they are located in the southern basin of the Caspian Sea. The performance of seven artificial neural network training algorithms, including Quick Propagation, Conjugate Gradient Descent, Quasi-Newton, Limited Memory Quasi-Newton, Levenberg–Marquardt, Online Back Propagation, and Batch Back Propagation, has been evaluated to predict the water quality index. The results show that the highest mean absolute error was observed in the WQI, as predicted by the ANN LM training algorithm; the lowest error values were for the ANN LMQN and CGD training algorithms. Our findings also indicate that for upscaling, the aggregated MLR model could provide reliable performance to predict the water quality index, since the r2 coefficient of the models varies from 0.73 ± 0.2 for large catchments, to 0.85 ± 0.15 for very large catchments, and for downscaling, the r2 coefficient of the disaggregated MLR model ranges from 0.93 ± 0.05 for very large catchments, to 0.97 ± 0.02 for medium catchments. Therefore, scaled models could be applied to catchments that lack sufficient data to perform a rapid assessment of the water quality index in the study area.

Bacterial prediction using internet of things (IoT) and machine learning

Water is a basic and primary resource which is required for sustenance of life on the Earth. The importance of water quality is increasing with the ascending water pollution owing to industrialization and depletion of fresh water sources. The countries having low control on reducing water pollution are likely to retain poor public health. Additionally, the methods being used in most developing countries are not effective and are based more on human intervention than on technological and automated solutions. Typically, most of the water samples and related data are monitored and tested in laboratories, which eventually consumes time and effort at the expense of producing fewer reliable results. In view of the above, there is an imperative need to devise a proper and systematic system to regularly monitor and manage the quality of water resources to arrest the related issues. Towards such ends, Internet of Things (IoT) is a great alternative to such traditional approaches which are complex and ineffective and it allows taking remote measurements in real-time with minimal human involvement. The proposed system consists of various water quality measuring nodes encompassing various sensors including dissolved oxygen, turbidity, pH level, water temperature, and total dissolved solids. These sensors nodes deployed at various sites of the study area transmit data to the server for processing and analysis using GSM modules. The data collected over months is used for water quality classification using water quality indices and for bacterial prediction by employing machine learning algorithms. For data visualization, a Web portal is developed which consists of a dashboard of Web services to display the heat maps and other related info-graphics. The real-time water quality data is collected using IoT nodes and the historic data is acquired from the Rawal Lake Filtration Plant. Several machine learning algorithms including neural networks (NN), convolutional neural networks (CNN), ridge regression (RR), support vector machines (SVM), decision tree regression (DTR), Bayesian regression (BR), and an ensemble of all models are trained for fecal coliform bacterial prediction, where SVM and Bayesian regression models have shown the optimal performance with mean squared error (MSE) of 0.35575 and 0.39566 respectively. The proposed system provides an alternative and more convenient solution for bacterial prediction, which otherwise is done manually in labs and is an expensive and time-consuming approach. In addition to this, it offers several other advantages including remote monitoring, ease of scalability, real-time status of water quality, and a portable hardware.

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.

Prediction of groundwater quality using efficient machine learning technique

To ensure safe drinking water sources in the future, it is imperative to understand the quality and pollution level of existing groundwater. The prediction of water quality with high accuracy is the key to control water pollution and the improvement of water management. In this study, a deep learning (DL) based model is proposed for predicting groundwater quality and compared with three other machine learning (ML) models, namely, random forest (RF), eXtreme gradient boosting (XGBoost), and artificial neural network (ANN). A total of 226 groundwater samples are collected from an agriculturally intensive area Arang of Raipur district, Chhattisgarh, India, and various physicochemical parameters are measured to compute entropy weight-based groundwater quality index (EWQI). Prediction performances of models are determined by introducing five error metrics. Results showed that DL model is the best prediction model with the highest accuracy in terms of R², i.e., R² = 0996 against the RF (R² = 0.886), XGBoost (R² = 0.0.927), and ANN (R² = 0.917). The uncertainty of the DL model output is cross-verified by running the proposed algorithm with newly randomized dataset for ten times, where minor deviations in the mean value of performance metrics are observed. Moreover, input variable importance computed by prediction models highlights that DL model is the most realistic and accurate approach in the prediction of groundwater quality.

Modelling and Prediction of Water Quality by Using Artificial Intelligence

Artificial intelligence methods can remarkably reduce costs for water supply and sanitation systems and help ensure compliance with the quality of drinking and wastewater treatment. Therefore, modelling and predicting water quality to control water pollution has been widely researched. The novelty of the proposed system is presented to develop an efficient operation of monitoring drinking water to ensure a sustainable and friendly green environment. In this work, the adaptive neuro-fuzzy inference system (ANFIS) algorithm was developed to predict the water quality index (WQI). Feed-forward neural network (FFNN) and K-nearest neighbors were applied to classify water quality. The dataset has eight significant parameters, but seven parameters were considered to show significant values. The proposed methodology was developed based on these statistical parameters. Prediction results demonstrated that the ANFIS model was superior for the prediction of WQI values. Nevertheless, the FFNN algorithm achieved the highest accuracy (100%) for water quality classification (WQC). Furthermore, the ANFIS model accurately predicted WQI, and the FFNN model showed superior robustness in classifying the WQC. In addition, the ANFIS model showed accuracy during the testing phase, with a regression coefficient of 96.17% for predicting WQI, and the FFNN model achieved the highest accuracy (100%) for WQC. This proposed method, using advanced artificial intelligence, can aid in water treatment and management.

Water Quality Prediction Using Artificial Intelligence Algorithms

During the last years, water quality has been threatened by various pollutants. Therefore, modeling and predicting water quality have become very important in controlling water pollution. In this work, advanced artificial intelligence (AI) algorithms are developed to predict water quality index (WQI) and water quality classification (WQC). For the WQI prediction, artificial neural network models, namely nonlinear autoregressive neural network (NARNET) and long short-term memory (LSTM) deep learning algorithm, have been developed. In addition, three machine learning algorithms, namely, support vector machine (SVM), K-nearest neighbor (K-NN), and Naive Bayes, have been used for the WQC forecasting. The used dataset has 7 significant parameters, and the developed models were evaluated based on some statistical parameters. The results revealed that the proposed models can accurately predict WQI and classify the water quality according to superior robustness. Prediction results demonstrated that the NARNET model performed slightly better than the LSTM for the prediction of the WQI values and the SVM algorithm has achieved the highest accuracy (97.01%) for the WQC prediction. Furthermore, the NARNET and LSTM models have achieved similar accuracy for the testing phase with a slight difference in the regression coefficient (RNARNET = 96.17% and RLSTM = 94.21%). This kind of promising research can contribute significantly to water management.

A Generalized Method for Modeling the Adsorption of Heavy Metals with Machine Learning Algorithms

Applications of machine learning algorithms (MLAs) to modeling the adsorption efficiencies of different heavy metals have been limited by the adsorbate–adsorbent pair and the selection of specific MLAs. In the current study, adsorption efficiencies of fourteen heavy metal–adsorbent (HM-AD) pairs were modeled with a variety of ML models such as support vector regression with polynomial and radial basis function kernels, random forest (RF), stochastic gradient boosting, and bayesian additive regression tree (BART). The wet experiment-based actual measurements were supplemented with synthetic data samples. The first batch of dry experiments was performed to model the removal efficiency of an HM with a specific AD. The ML modeling was then implemented on the whole dataset to develop a generalized model. A ten-fold cross-validation method was used for the model selection, while the comparative performance of the MLAs was evaluated with statistical metrics comprising Spearman’s rank correlation coefficient, coefficient of determination (R2), mean absolute error, and root-mean-squared-error. The regression tree methods, BART, and RF demonstrated the most robust and optimum performance with 0.96 ⫹ R2 ⫹ 0.99. The current study provides a generalized methodology to implement ML in modeling the efficiency of not only a specific adsorption process but also a group of comparable processes involving multiple HM-AD pairs.

Water quality index and sanitary and socioeconomic indicators in Minas Gerais, Brazil

Minas Gerais is one of the 27 federative units of Brazil; it is the fourth state with the largest territorial area and the second in number of inhabitants. Since 1997, the monitoring of the surface water quality of the State of Minas Gerais has been carried out. In this study, generalized regression models were constructed to determine the correlation between the Water Quality Index (WQI) and the sanitary and socioeconomic variables: Municipal Population, Human Development Index (HDI), Gini Index, Percentage of Vulnerables to Poverty (Poverty), Monthly Per Capita Income, Percentage of Inadequate or Poor Sanitation. In addition to the sanitary and socioeconomic variables listed, it also used year of water quality monitoring, altitude of the monitoring point, and distance from the monitoring point to the urban center of the municipality. The results from the generalized models showed that the variables year, altitude, Gini Index, monthly per capita income, and poor sanitation variables were positively associated with WQI. In other words, high values of each variable increased WQI, while population variables HDI and poverty were negatively related to WQI, that is, a high population value, HDI, or poverty implies a low WQI value. Socioeconomic variables such as HDI, Gini Index, poorness, or poor sanitation percentage present the coefficients with the largest modulus. Thus, among the socioeconomic variables studied, these are the ones that most contribute to the variability of WQI. The year and altitude variables have positive regression coefficients, indicating that when these variables increase, WQI also increases. The positive correlation with the year shows that the surface water quality of Minas Gerais improved during the monitoring years.

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

River water quality assessment is one of the most important tasks to enhance water resources management plans. A water quality index (WQI) considers several water quality variables simultaneously. Traditionally WQI calculations consume time and are often fraught with errors during derivations of sub-indices. In this study, 4 standalone (random forest (RF), M5P, random tree (RT), and reduced error pruning tree (REPT)) and 12 hybrid data-mining algorithms (combinations of standalones with bagging (BA), CV parameter selection (CVPS) and randomizable filtered classification (RFC)) were used to create Iran WQI (IRWQI_sc) predictions. Six years (2012 to 2018) of monthly data from two water quality monitoring stations within the Talar catchment were compiled. Using Pearson correlation coefficients, 10 different input combinations were constructed. The data were divided into two groups (ratio 70:30) for model building (training dataset) and model validation (testing dataset) using a 10-fold cross-validation technique. The models were evaluated using several statistical and visual evaluation metrics. Result show that fecal coliform (FC) and total solids (TS) had the greatest and least effect on the prediction of IRWQI_sc. The best input combinations varied among the algorithms; generally variables with very low correlations displayed weaker performance. Hybrid algorithms improved the prediction power of several of the standalone models, but not all. Hybrid BA-RT outperformed the other models (R² = 0.941, RMSE = 2.71, MAE = 1.87, NSE = 0.941, PBIAS = 0.500). PBIAS indicated that all algorithms, with the exceptions of RT, BA-RT and CVPS-REPT, overestimated WQI values.

Efficient Water Quality Prediction Using Supervised Machine Learning

Water makes up about 70% of the earth’s surface and is one of the most important sources vital to sustaining life. Rapid urbanization and industrialization have led to a deterioration of water quality at an alarming rate, resulting in harrowing diseases. Water quality has been conventionally estimated through expensive and time-consuming lab and statistical analyses, which render the contemporary notion of real-time monitoring moot. The alarming consequences of poor water quality necessitate an alternative method, which is quicker and inexpensive. With this motivation, this research explores a series of supervised machine learning algorithms to estimate the water quality index (WQI), which is a singular index to describe the general quality of water, and the water quality class (WQC), which is a distinctive class defined on the basis of the WQI. The proposed methodology employs four input parameters, namely, temperature, turbidity, pH and total dissolved solids. Of all the employed algorithms, gradient boosting, with a learning rate of 0.1 and polynomial regression, with a degree of 2, predict the WQI most efficiently, having a mean absolute error (MAE) of 1.9642 and 2.7273, respectively. Whereas multi-layer perceptron (MLP), with a configuration of (3, 7), classifies the WQC most efficiently, with an accuracy of 0.8507. The proposed methodology achieves reasonable accuracy using a minimal number of parameters to validate the possibility of its use in real time water quality detection systems.

Multivariate Temporal Convolutional Network: A Deep Neural Networks Approach for Multivariate Time Series Forecasting

Multivariable time series prediction has been widely studied in power energy, aerology, meteorology, finance, transportation, etc. Traditional modeling methods have complex patterns and are inefficient to capture long-term multivariate dependencies of data for desired forecasting accuracy. To address such concerns, various deep learning models based on Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) methods are proposed. To improve the prediction accuracy and minimize the multivariate time series data dependence for aperiodic data, in this article, Beijing PM2.5 and ISO-NE Dataset are analyzed by a novel Multivariate Temporal Convolution Network (M-TCN) model. In this model, multi-variable time series prediction is constructed as a sequence-to-sequence scenario for non-periodic datasets. The multichannel residual blocks in parallel with asymmetric structure based on deep convolution neural network is proposed. The results are compared with rich competitive algorithms of long short term memory (LSTM), convolutional LSTM (ConvLSTM), Temporal Convolution Network (TCN) and Multivariate Attention LSTM-FCN (MALSTM-FCN), which indicate significant improvement of prediction accuracy, robust and generalization of our model.

Prediction of annual drinking water quality reduction based on Groundwater Resource Index using the artificial neural network and fuzzy clustering

Drought is one of the most significant natural phenomena affecting different aspects of human life and the environment. Due to water scarcity, prediction of water quality reduction is very crucial for urban and rural communities. This study contributes by applying artificial neural network and modified fuzzy clustering techniques to estimate the drops in potential drinking water quality in the GIS environment. In this research, the probability of occurrence of adverse annual changes in the water quality of drinking water is estimated. The model was tested using real instances of the southeast aquifers, the regions of the central parts of the IRAN and especially the significant portions of the aquifers of the east area. To validate the model, the data adequacy test and the standardization of the drought index are used. The results of the lowest available water quality and the highest drought using ANNs show that the qualitative stress conditions in large part of the country's aquifers are in unfavorable conditions. Evidence from this research shows that the aquifers in these areas are expected to have severe drought stress and poor quality class status. Also, the computational results indicate that the modified clustering method increases the efficiency of the prediction model as against the previous research. The outcomes do not show a relatively favorable state of drinking water quality for some aquifers in the country. However, the conditions for quantitative changes in the depth of water, based on the predicted results of ANN, are considered critical. The generated maps demonstrate that about 64% of the study area is subjected to a severe deterioration in the quality of drinking water if the current trend continues in the exploitation of aquifers. As a result, the main finding the present study is that the probability of groundwater quality decline is significant in many aquifers in the country.

Large-scale association analysis of climate drought and decline in groundwater quantity using Gaussian process classification (case study: 609 study area of Iran)

BACKGROUND

The level of groundwater resources is changing rapidly and this requires the discovery of newer groundwater resources. Drought is one of the most significant natural phenomena affecting different aspects of human life and environment. During the last decades, the application of artificial intelligent techniques has been recognized as effective approaches to forecast an annual precipitation rate.

METHOD

In this study, the association analysis of climate drought and a decline in groundwater level is addressed using Gaussian process classification (GPC) and backpropagation (BP) artificial neural network (ANN). This methodology is proposed to create a framework for decision making and reduce uncertainty in water resource management calculations, and in particular to optimize the management of groundwater drinking water sources.

RESULTS

Underground water levels in 609 study plains in Iran were used to predict drought over the test period, extending from 2017 to 2021. The artificial intelligence methods were implemented in the Python programming environment to achieve an annual precipitation rate. A statistical summary of the Rasterized Cells of the zoning maps was used to validate the prediction results. Considering the relationship between water quality reductions and drought in Iranian aquifers due to the occurrence of groundwater drought periods, the results were validated by analysis of the effect of climate drought using the Standardized Precipitation Index (SPI) on the occurrence of observed droughts with the Groundwater Resources Index (GRI). The results are well-illustrated by the observation of the predicted digits in the third dimension of the Gaussian distribution.

CONCLUSION

According to the SPI indicator, the southern regions of the country, and especially the central parts of the plain, can be considered the most affected areas by the most severe future droughts. The prediction results indicate a decrease in drought severity as part of a two-year sequence involving a recurrence of drought exacerbation and relative decline, as well as a failed state after the critical condition of aquifers.