Prediction Of Water Quality Index Research Articles

AI-Powered Water Quality Index Prediction: Unveiling Machine Learning Precision in Hyper-Arid Regions

AI-Powered Water Quality Index Prediction: Unveiling Machine Learning Precision in Hyper-Arid Regions

Performance of Machine Learning, Artificial Neural Network (ANN), and stacked ensemble models in predicting Water Quality Index (WQI) from surface water quality parameters, climatic and land use data

Assessing water quality is essential for managing freshwater resources, safeguarding ecosystems, and guaranteeing public health. Traditional water quality assessment methods suffer from seasonal sampling, multi-parameter requirements, and labor-intensive sampling processes, which are major constraints for the frequent monitoring of vast river basins. To overcome this issue, the study modeled the remote sensing-based climatic and land use parameters with Principal Component Analysis (PCA) to leverage Artificial Neural Networks (ANN) and machine learning (ML) algorithms to predict the Water Quality Index (WQI). The Weighted Arithmetic Water Quality Index (WAWQI) method was used to calculate the WQI of the Godavari River Basin for the available 19 stream water quality parameters (SWQPs). Further, PCA was applied to reduce the dimensionality of the parameters from 19 to 6. These results led to the development of two modeling methods to predict the WQI. In the first method, the correlation-based model was developed to predict WQI by evaluating six SWQPs. The second method, the causal-effect model, uses land use and meteorological factors to determine WQI using causality. Using advanced AutoML techniques, the initial pool of 40 ML models was meticulously evaluated and refined, culminating in the selection of the top three exemplary models such as Extreme Gradient Boosting (XGB), Extra Trees (ET), and Random Forest (RF). In both methods, XGB models show better prediction, with the coefficient of determination (R2) value of 0.95 during training and 0.83 during testing in method one. Whereas in the second method, R2 of 0.93 in training and 0.80 in testing are obtained. Further, XGB, ET, and ANN outputs were stacked with each model to enhance these results in both methods. Among these three stacked models, the stacked ANN_ML model performed better compared to stacked XGB_ML and stacked ET_ML. In the first method, the stacked ANN_ML model predicts R2 values of 0.95 and 0.91 for training and testing. In the second method, 0.95 and 0.90 for training and testing are obtained using stacked ANN_ML model. These findings emphasize the stacked model prediction ability to capture nonlinear relationships in the parameters and the novel approach of land use and climate parameters based WQI prediction, which replace the laborious, time-consuming SWQP measurements.

Assessment and prediction of Water Quality Index (WQI) by seasonal key water parameters in a coastal city: application of machine learning models.

The Water Quality Index (WQI) provides comprehensive assessments in river systems; however, its calculation involves numerous water quality parameters, costly in sample collection and laboratory analysis. The study aimed to determine key water parameters and the most reliable models, considering seasonal variations in the water environment, to maximize the precision of WQI prediction by a minimal set of water parameters. Ten statistical or machine learning models were developed to predict the WQI over four seasons using water quality dataset collected in a coastal city adjacent to the Yellow Sea in China, based on which the key water parameters were identified and the variations were assessed by the Seasonal-Trend decomposition procedure based on Loess (STL). Results indicated that model performance generally improved with adding more input variables except Self-Organizing Map (SOM). Tree-based ensemble methods like Extreme Gradient Boosting (XGB) and Random Forest (RF) demonstrated the highest accuracy, particularly in winter. Nutrients (Ammonia Nitrogen (AN) and Total Phosphorus (TP)), Dissolved Oxygen (DO), and turbidity were determined as key water parameters, based on which, the prediction accuracy for Medium and Low grades was perfect while it was over 80% for the Good grade in spring and winter and dropped to around 70% in summer and autumn. Nutrient concentrations were higher at inland stations; however, it worsened at coastal stations, especially in summer. The study underscores the importance of reliable WQI prediction models in water quality assessment, especially when data is limited, which are crucial for managing water resources effectively.

An efficient water quality index forecasting and categorization using optimized Deep Capsule Crystal Edge Graph neural network.

The world's freshwater supply, predominantly sourced from rivers, faces significant contamination from various economic activities, confirming that the quality of river water is critical for public health, environmental sustainability, and effective pollution control. This research addresses the urgent need for accurate and reliable water quality monitoring by introducing a novel method for estimating the water quality index (WQI). The proposed approach combines cutting-edge optimization techniques with Deep Capsule Crystal Edge Graph neural networks, marking a significant advancement in the field. The innovation lies in the integration of a Hybrid Crested Porcupine Genghis Khan Shark Optimization Algorithm for precise feature selection, ensuring that the most relevant indicators of water quality (WQ) are utilized. Furthermore, the use of the Greylag Goose Optimization Algorithm to fine-tune the neural network's weight parameters enhances the model's predictive accuracy. This dual optimization framework significantly improves WQI prediction, achieving a remarkable mean squared error (MSE) of 6.7 and an accuracy of 99%. By providing a robust and highly accurate method for WQ assessment, this research offers a powerful tool for environmental authorities to proactively manage river WQ, prevent pollution, and evaluate the success of restoration efforts. PRACTITIONER POINTS: Novel method combines optimization and Deep Capsule Crystal Edge Graph for WQI estimation. Preprocessing includes data cleanup and feature selection using advanced algorithms. Deep Capsule Crystal Edge Graph neural network predicts WQI with high accuracy. Greylag Goose Optimization fine-tunes network parameters for precise forecasts. Proposed method achieves low MSE of 6.7 and high accuracy of 99%.

Assessment and prediction of water quality indices by machine learning-genetic algorithm and response surface methodology

Assessment and prediction of water quality indices by machine learning-genetic algorithm and response surface methodology

Groundwater quality assessment using machine learning models: a comprehensive study on the industrial corridor of a semi-arid region.

Water plays a significant role in sustaining the lives of humans and other living organisms. Groundwater quality analysis has become inevitable, because of increased contamination of water resources and global warming. This study used machine learning (ML) models to predict the water quality index (WQI) and water quality classification (WQC). Forty groundwater samples were collected near the Ranipet industrial corridor, and the hydrogeochemistry and heavy metal contamination were analyzed. WQC prediction employed random forest (RF), gradient boosting (GB), decision tree (DT), and K-nearest neighbor (KNN) models, and WQI prediction used extreme gradient boosting (XGBoost), support vector regressor (SVR), RF, and multi-layer perceptron (MLP) models. The grid search method is used to evaluate the ML model by F1 score, accuracy, recall, precision, and Matthews correlation coefficient (MCC) for WQC and the coefficient of determination (R2), mean absolute error (MAE), mean square error (MSE), and median absolute percentage error (MAPE) for WQI. The WQI results indicate that the groundwater quality of the study area is very poor and unsuitable for drinking or irrigation purposes. The performance metrics of the RF model excelled in predicting both WQC (accuracy = 97%) and WQI (R2 = 91.0%), outperforming other models and emphasizing ML's superiority in groundwater quality assessment. The findings suggest that ML models perform well and yield better accuracy than conventional techniques used in groundwater quality assessment studies.

Surface water quality prediction in the lower Thoubal river watershed, India: A hyper-tuned machine learning approach and DNN-based sensitivity analysis

The accurate and efficient monitoring, assessment, and prediction of water resources is crucial for the sustenance of life and health of the environment. Traditional methods for assessing water quality can be laborious and time-consuming, but the use of machine learning algorithms can improve the speed and precision of these predictions. The study aimed to evaluate the quality of surface water in the lower Thoubal river using a comprehensive approach that analyzed 12 physico-chemical properties of the water samples collected from 16 different sites. Four machine learning algorithms namely, deep neural network (DNN), gradient boost model (GBM), generalized linear model (GLM), and random forest (RF) were applied and compared for the prediction efficiency of a water quality index (WQI). Sensitivity and uncertainty analysis of the parameters were carried out using a DNN-based model. The study incorporated model evaluation using learning curve along with five key performance assessors. The findings revealed marked variation in the WQI in the watershed. Of the samples tested, about 70 % were found to have good water quality, 18 % had excellent water quality, 11 % had poor water quality, 1 % had very poor water quality, and less than 1 % were deemed unsuitable. Turbidity, BOD, and COD were found to be the most influential parameter towards WQI prediction. The effectiveness of the predictive models ranked as GLM>DNN>RF>GBM, GLM>GBM>DNN>RF, and GLM>DNN>RF>GBM; based on testing, training and 5-fold cross-validation. The GLM model consistently showed superior performance with the lowest RMSEs of 0.14783 in training, 0.15936 in validation, and 0.26115 in 5-fold cross-validation. Conversely, during the training, RF displayed the worst RMSEs of 5.38354, 4.79754 by GBM in validation, and 7.04887 by GBM in 5-fold cross-validation. The analysis using the Taylor diagram and learning curve further supported that GLM was the most effective model for predicting surface water quality. Biological oxygen demand, chemical oxygen demand, electrical conductivity, turbidity and total hardness were found to be the key parameters influencing the water quality. Unregulated and illegal sand mining from the riverbed, garbage dumping into the water bodies, runoffs from agricultural fields have significantly deteriorated the water quality in the study area. The scalable approach of the study and its results can benefit the local water managers and water research community.

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.

Water Quality Index Prediction and Classification using Hyperparameter Tuned Deep Learning Approach

<p>Water quality (WQ) is hugely important for animals, humans, plants, industries, and the environment. In the past few years, the WQ has been compressed by pollution and contamination. Usually, WQ is assessed utilizing costly laboratory and arithmetical processes, making real observation ineffective. Whereas, the poor WQ wants a more real and cost-effective resolution. Water pollution is a critical problem, so, it is vital to generate a method that estimates WQ in order to manage water pollution and notify users on the occasion of the recognition of poor water superiority. For effectual WQ management, it is vital to precisely estimate the WQ type. We use the advantage of machine learning (ML) models to build a model proficient in forecasting the WQ index and class. Therefore, this paper presents an automated Water Quality Index Prediction and Classification using Hyperparameter Tuned Deep Learning (WQIPC-HTDL) Approach. The purpose of the WQIPC-HTDL technique is to estimate WQI and classify the WQ into multiple levels. In the WQIPC-HTDL technique, the linear scaling normalization (LSN) approach is used. Besides, the long short-term memory (LSTM) technique is employed for the prediction and classification process. To enhance the efficacy of the LSTM model, the grasshopper optimizer algorithm (GOA) can be used. To point out the enhanced performance of the WQIPC-HTDL technique, a detailed simulation analysis was made. The obtained values inferred the rule of the WQIPC-HTDL technique when equated to other models.</p>

Monitoring the Industrial waste polluted stream - Integrated analytics and machine learning for water quality index assessment

The Water Quality Index (WQI) is a primary metric used to evaluate and categorize surface water quality which plays a crucial role in the management of fresh water resources. Machine Learning (ML) modeling offers potential insights into water quality index prediction. This study employed advanced ML models to get potential insights into the prediction of water quality index for the Aik-Stream, an industrially polluted natural water resource in Pakistan with 19 input water quality variables aligning them with surrounding land use and anthropogenic activities. Six machine learning algorithms, i.e. Adaptive Boosting (AdaBoost), K-Nearest Neighbors (K-NN), Gradient Boosting (GB), Random Forests (RF), Support Vector Regression (SVR), and Bayesian Regression (BR) were employed as benchmark models to predict the Water Quality Index (WQI) values of the polluted stream to achieve our objectives. For model calibration, 80% of the dataset was reserved for training, while 20% was set aside for testing. In our comparative analyses of predictive models for water quality index, the Gradient Boost (GB) model stood out the fittest for its precision, utilizing a combination of just seven parameters (chemical oxygen demand, total organic carbon, oil & grease, Ammonia- nitrogen, arsenic, nickel and zinc), surpassing other models by achieving better results in both training (R2 = 0.88, RMSE = 7.24) and testing (R2 = 0.85, RMSE = 8.67). Analyzing feature importance showed that all the selected variables, except for NO3 N, TDS and temperature had an impact on the accuracy of the models predictions. It is concluded that the application of machine learning to assess water quality in polluted environments enhances accuracy and facilitates real-time tracking, enabling proactive risk mitigations.

Machine Learning Models for Water Quality Prediction: A Comprehensive Analysis and Uncertainty Assessment in Mirpurkhas, Sindh, Pakistan

Groundwater represents a pivotal asset in conserving natural water reservoirs for potable consumption, irrigation, and diverse industrial uses. Nevertheless, human activities intertwined with industry and agriculture contribute significantly to groundwater contamination, highlighting the critical necessity of appraising water quality for safe drinking and effective irrigation. This research primarily focused on employing the Water Quality Index (WQI) to gauge water’s appropriateness for these purposes. However, the generation of an accurate WQI can prove time-intensive owing to potential errors in sub-index calculations. In response to this challenge, an artificial intelligence (AI) forecasting model was devised, aiming to streamline the process while mitigating errors. The study collected 422 data samples from Mirpurkash, a city nestled in the province of Sindh, for a comprehensive exploration of the region’s WQI attributes. Furthermore, the study probed into unraveling the interdependencies amidst variables in the physiochemical analysis of water. Diverse machine learning classifiers were employed for WQI prediction, with findings revealing that Random Forest and Gradient Boosting lead with 95% and 96% accuracy, followed closely by SVM at 92%. KNN exhibits an accuracy rate of 84%, and Decision Trees achieve 77%. Traditional water quality assessment methods are time-consuming and error-prone; a transformative approach using artificial intelligence and machine learning addresses these limitations. In addition to WQI prediction, the study conducted an uncertainty analysis of the models using the R-factor, providing insights into the reliability and consistency of predictions. This dual approach, combining accurate WQI prediction with uncertainty assessment, contributes to a more comprehensive understanding of water quality in Mirpurkash and enhances the reliability of decision-making processes related to groundwater utilization.

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.

Research on water environmental indicators prediction method based on EEMD decomposition with CNN-BiLSTM.

Water resources protection is related to the development of the social economy, and the monitoring and prediction of water environmental indicators have important practical significance. In view of the seasonality, periodicity, uncertainty, and nonlinear characteristics of water quality indicators data, traditional prediction models have poor performance. To address this issue, this paper introduces a hybrid water quality index prediction model based on Ensemble Empirical Mode Decomposition (EEMD), combined with Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory Network (BiLSTM). We have conducted out experiments to predict dissolved oxygen based on the water quality monitoring indicators of the Liaohe National Control Sanhongcun Village station in Yichun City. The results show that the model proposed in this paper improves the [Formula: see text] index by 5%, 7% and 5% compared to the suboptimal model in the 4-h, 1-day and 2-day index predictions, respectively.

An Approach to Forecast Quality of Water Effectively Using Machine Learning Algorithms

Background: The quality of water directly or indirectly impacts the health and environmental well-being. Data about water quality can be evaluated using a Water Quality Index (WQI). Computing WQI is a quick and affordable technique to accurately summarise the quality of water. background: Find strategies for data preparation to categorize a dataset on the water quality in two remote Indian villages Objective: The objective of this study is to find strategies for data preparation to categorize a dataset on the water quality in two remote Indian villages in different geographic locations, to predict the quality of water, and to identify low-quality water before it is made accessible for human consumption. Methods: To accomplish this task, four water quality features Nitrate, pH, Residual Chlorine, and Total Dissolved Solids which are crucial for human consumption, are considered to dictate the quality of water. Methods used in handling these features include five steps that are data preprocessing with min-max normalization, finding WQI, using feature correlation to identify parameter importance with WQI, application of supervised machine learning regression models such as Random Forest (RF), Multiple Linear Regression (MLR), Gradient Boosting (GB) and Support Vector Machine (SVM) for WQI prediction. Then, a variety of machine learning classification models, including K-Nearest Neighbour (KNN), Support Vector Classifier (SVC), and Multi-layer Perceptron (MLP), are ensembled with Logistic Regression (LR), acting as a meta learner, to create a stack ensemble model classifier to predict the Water Quality Class (WQC) more accurately. Results: The examination of the testing model revealed that RF regression and MLR algorithms performed best in predicting the WQI with mean absolute error (MAE) of 0.003 and 0.001 respectively. Mean square error (MSE), root mean square error (RMSE), R squared (R2), and Explained Variance Score (EVS) findings are 0.002,0.005,0.988 and 0.998 respectively with RF while 0.001,0.031,0.999 and 0.999 respectively with MLR. Meanwhile, for predicting WQC, the stack model classifier showed the best performance with an Accuracy of 0.936, F1 score of 0.93, and Matthews Correlation Coefficient (MCC) of 0.893 for the dataset of Lalpura and Accuracy of 0.991, F1 Score of 0.991 and MCC of 0.981 respectively for the dataset of Heingang. Conclusion: This study explores a method for predicting water quality that combines easy and feasible water quality measurements with machine learning. The stack model classifier performed best for multiclass classification, according to this study. To ensure that the highest quality of water is given throughout the year, information from this study will motivate researchers to look into the underlying root causes of the quality variations.

A Comprehensive Review of Machine Learning for Water Quality Prediction over the Past Five Years

Water quality prediction, a well-established field with broad implications across various sectors, is thoroughly examined in this comprehensive review. Through an exhaustive analysis of over 170 studies conducted in the last five years, we focus on the application of machine learning for predicting water quality. The review begins by presenting the latest methodologies for acquiring water quality data. Categorizing machine learning-based predictions for water quality into two primary segments—indicator prediction and water quality index prediction—further distinguishes between single-indicator and multi-indicator predictions. A meticulous examination of each method’s technical details follows. This article explores current cutting-edge research trends in machine learning algorithms, providing a technical perspective on their application in water quality prediction. It investigates the utilization of algorithms in predicting water quality and concludes by highlighting significant challenges and future research directions. Emphasis is placed on key areas such as hydrodynamic water quality coupling, effective data processing and acquisition, and mitigating model uncertainty. The paper provides a detailed perspective on the present state of application and the principal characteristics of emerging technologies in water quality prediction.

Groundwater Quality Assessment and Irrigation Water Quality Index Prediction Using Machine Learning Algorithms

The evaluation of groundwater quality is crucial for irrigation purposes; however, due to financial constraints in developing countries, such evaluations suffer from insufficient sampling frequency, hindering comprehensive assessments. Therefore, associated with machine learning approaches and the irrigation water quality index (IWQI), this research aims to evaluate the groundwater quality in Naama, a region in southwest Algeria. Hydrochemical parameters (cations, anions, pH, and EC), qualitative indices (SAR,RSC,Na%,MH,and PI), as well as geospatial representations were used to determine the groundwater’s suitability for irrigation in the study area. In addition, efficient machine learning approaches for forecasting IWQI utilizing Extreme Gradient Boosting (XGBoost), Support vector regression (SVR), and K-Nearest Neighbours (KNN) models were implemented. In this research, 166 groundwater samples were used to calculate the irrigation index. The results showed that 42.18% of them were of excellent quality, 34.34% were of very good quality, 6.63% were good quality, 9.64% were satisfactory, and 4.21% were considered unsuitable for irrigation. On the other hand, results indicate that XGBoost excels in accuracy and stability, with a low RMSE (of 2.8272 and a high R of 0.9834. SVR with only four inputs (Ca2+, Mg2+, Na+, and K) demonstrates a notable predictive capability with a low RMSE of 2.6925 and a high R of 0.98738, while KNN showcases robust performance. The distinctions between these models have important implications for making informed decisions in agricultural water management and resource allocation within the region.

Modelling and Prediction of Water Quality Index of Selected Pond Water in Aboh Mbaise Local Government Area, Imo State, Nigeria

The aim of this study is the development of water quality models against water quality parameters from 5 selected ponds in Aboh-Mbaise local government area (LGA) of Imo state.
 Water quality index (WQI) as dependent variable computed based on water quality parameters which were taken as independent variables and modelled as multiple linear regression. Given that there are over 25 water quality parameters (physiochemical, heavy metals and microbials), it was necessary to adopt factor reduction technique using principal component analysis. In this approach, 3 principal component factors were generated having corresponding factors (independent variables of 5, 6 and 5 respectively). The resulting multiple regression for the 3 principal component factors yielded Goodness of Fit of 92.9, 99.0 and 96.6% as well as root mean square error (RMSE) of 66.673, 0.672 and 51.968 respectively.
 The model verification was accomplished by plotting the computed WQI against predicted values from the developed models and the best option was the one with 99.0% R2 value with the following independent variables-sulphate, TSS, phosphate, turbidity, total solid and nitrates.
 The model output is relevant in WQI prediction given the applicable water quality characteristics.
 This predictive model will find wide application in selecting water treatment options for pond water in the study area.

Enhancing river health monitoring: Developing a reliable predictive model and mitigation plan

The escalating environmental harm inflicted upon rivers is an unavoidable outcome resulting from climate fluctuations and anthropogenic activities, leading to a catastrophic impact on water quality and thousands of individuals succumb to waterborne diseases. Consequently, the water quality monitoring stations have been established worldwide. Regrettably, the real-time evaluation of Water Quality Index (WQI) is hindered by the intricate nature of off-site water quality parameters. Thus, there is a pressing need to create a precise and robust water quality prediction model. The dynamic and non-linear characteristics of water quality parameters pose significant challenges for conventional machine learning algorithms like multi-linear regression, as they struggle to capture these complexities. In this particular investigation, machine learning model called Feedforward Artificial Neural Networks (FANNs) was employed to develop WQI prediction model of Batu Pahat River, Malaysia exclusively utilizing on-site parameters. The proposed method involves a consideration of whether to include or exclude parameters such as BOD and COD, which are not measured in real time and can be costly to monitor as model inputs. Validation accuracy values of 99.53%, 97.99%, and 91.03% were achieved in three different scenarios: the first scenario utilized the full input, the second scenario excluded BOD, and the third scenario excluded both BOD and COD. It was suggested that the model has better predictive power between input variables and output variables. Factor contributed to river pollution has been identified and mitigation plan for Batu Pahat river pollution has been proposed. This could provide an effective alternative to compute the pollution, better manage water resources and mitigate negative impacts of climate change of river ecosystems.

Coupling Machine and Deep Learning with Explainable Artificial Intelligence for Improving Prediction of Groundwater Quality and Decision-Making in Arid Region, Saudi Arabia

Recently, machine learning (ML) and deep learning (DL) models based on artificial intelligence (AI) have emerged as fast and reliable tools for predicting water quality index (WQI) in various regions worldwide. In this study, we propose a novel stacking framework based on DL models for WQI prediction, employing a convolutional neural network (CNN) model. Additionally, we introduce explainable AI (XAI) through XGBoost-based SHAP (SHapley Additive exPlanations) values to gain valuable insights that can enhance decision-making strategies in water management. Our findings demonstrate that the stacking model achieves the highest accuracy in WQI prediction (R2: 0.99, MAPE: 15.99%), outperforming the CNN model (R2: 0.90, MAPE: 58.97%). Although the CNN model shows a relatively high R2 value, other statistical measures indicate that it is actually the worst-performing model among the five tested. This discrepancy may be attributed to the limited training data available for the CNN model. Furthermore, the application of explainable AI (XAI) techniques, specifically XGBoost-based SHAP values, allows us to gain deep insights into the models and extract valuable information for water management purposes. The SHAP values and interaction plot reveal that elevated levels of total dissolved solids (TDS), zinc, and electrical conductivity (EC) are the primary drivers of poor water quality. These parameters exhibit a nonlinear relationship with the water quality index, implying that even minor increases in their concentrations can significantly impact water quality. Overall, this study presents a comprehensive and integrated approach to water management, emphasizing the need for collaborative efforts among all stakeholders to mitigate pollution levels and uphold water quality. By leveraging AI and XAI, our proposed framework not only provides a powerful tool for accurate WQI prediction but also offers deep insights into the models, enabling informed decision-making in water management strategies.

Metaheuristic approaches for prediction of water quality indices with relief algorithm-based feature selection

Monitoring and assessing groundwater quality are important for sustainable water resource management. Therefore, the present study aimed to analyze and predict the water quality indices using metaheuristics algorithms. Groundwater samples were acquired from 26 tube wells periodically, and laboratory analysis was performed in the soil and water laboratory, Water Technology Centre (WTC), ICAR-Indian Agricultural Research Institute (ICAR-IARI), New Delhi, India. Water quality parameters such as pH, electrical conductivity (EC), calcium (Ca2+), magnesium (Mg2+), sodium (Na+), potassium (K+), chloride (Cl−), carbonate (CO32−), bicarbonate (HCO3−) and sulphate (SO42−) were used for the computation of water quality indices. The relief algorithm was used for the selection of the best input combinations. Seven meta heuristics algorithms viz., Reduced Error Pruning Tree (REPTree), Random Forest (RF), Bagging, Additive Regression (AdR), M5Preuning Tree (M5P), Random Subspace (RSS), and Artificial Neural Networks (ANN) were trained and tested for Water Quality Index (WQI), and the best four candidate models, i.e., M5P, ANN, RF, and RSS were validated for the prediction of Kelly ratio (KR), sodium adsorption ratio (SAR), permeability index (PI), and percent sodium (%Na). The performance of models was assessed using statistical indicators, i.e., mean absolute error (MAE), root mean squared error (RMSE), Willmott index (WI), Nash-Sutcliffe efficiency (NSE), coefficient of correlation (R), and visual graphics (i.e., Taylor diagram and radar chart). On a quantitative comparative scale, the M5P model performed better for predicting WQI (WI = 0.997, NSE = 0.988, MAE = 7.173, RMSE = 10.769 and R = 0.999) and % Na (WI = 901, NSE = 0.692, MAE = 3.817, RMSE = 5.005 and R = 0.853). The ANN model had a significant edge over other models for predicting indices namely, KR (WI = 0.844, NSE = 0.625, MAE = 0.121, RMSE = 0.164 and R = 0.825), PI (WI = 0.987, NSE = 0.949, MAE = 2.508, RMSE = 3.269 and, R = 0.981), and SAR (WI = 0.981, NSE = 0.916, MAE = 0.314, RMSE = 0.385 and R = 0.976). The result indicated that the M5P model has the potential to predict the water quality indices even under limited input parameters scenarios in the sub-tropical climatic condition. Also, study findings revealed that the relief algorithm based input feature selection could be used for assessing water quality indices. The outcome of the present study would be of interest to policymakers or irrigation planners to achieve groundwater sustainability in terms of water quality suitability for distinct applications and remedial measures to enhance groundwater quality.