Exploring Artificial Intelligence Techniques for Groundwater Quality Assessment

Agrawal, Purushottam; Sinha, Alok; Kumar, Satish; Agarwal, Ankit; Banerjee, Ashes; Villuri, Vasanta Govind Kumar; Annavarapu, Chandra Sekhara Rao; Dwivedi, Rajesh; Dera, Vijaya Vardhan Reddy; Sinha, Jitendra; Pasupuleti, Srinivas

doi:10.3390/w13091172

Cited by 48 publications

(21 citation statements)

References 54 publications

(47 reference statements)

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“…A sum of 95 (pre-and postmonsoon season) water samples were used to train and test the selected models. Agrawal et al [49] conducted a study on artificial intelligence approaches for groundwater quality evaluation in the Pindrawan tank command region in Chhattisgarh's upper Mahanadi River valley (southeastern section), Raipur district. Groundwater samples were acquired from 37 sites.…”

Section: Discussionmentioning

confidence: 99%

Predicting Irrigation Water Quality Indices Based on Data-Driven Algorithms: Case Study in Semiarid Environment

Dimple

Rajput

Al‐Ansari

et al. 2022

Journal of Chemistry

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Ascertaining water quality for irrigational use by employing conventional methods is often time taking and expensive due to the determination of multiple parameters needed, especially in developing countries. Therefore, constructing precise and adequate models may be beneficial in resolving this problem in agricultural water management to determine the suitable water quality classes for optimal crop yield production. To achieve this objective, five machine learning (ML) models, namely linear regression (LR), random subspace (RSS), additive regression (AR), reduced error pruning tree (REPTree), and support vector machine (SVM), have been developed and tested for predicting of six irrigation water quality (IWQ) indices such as sodium adsorption ratio (SAR), percent sodium (%Na), permeability index (PI), Kelly ratio (KR), soluble sodium percentage (SSP), and magnesium hazards (MH) in groundwater of the Nand Samand catchment of Rajasthan. The accuracy of these models was determined serially using the mean squared error (MSE), correlation coefficients (r), mean absolute error (MAE), and root mean square error (RMSE). The SVM model showed the best-fit model for all irrigation indices during testing, that is, RMSE: 0.0662, 4.0568, 3.0168, 0.1113, 3.7046, and 5.1066; r: 0.9364, 0.9618, 0.9588, 0.9819, 0.9547, and 0.8903; MSE: 0.004381, 16.45781, 9.101218, 0.012383, 13.72447, and 26.078; MAE: 0.042, 3.1999, 2.3584, 0.0726, 2.9603, and 4.0582 for KR, MH, SSP, SAR, %Na, and PI, respectively. The KR and SAR values were predicted accurately by the SVM model in comparison to the observed values. As a result, machine learning algorithms can improve irrigation water quality characteristics, which is critical for farmers and crop management in various irrigation procedures. Additionally, the findings of this research suggest that ML models are effective tools for reliably predicting groundwater quality using general water quality parameters that may be acquired directly on periodical basis. Assessment of water quality indices may also help in deriving optimal strategies to utilise inferior quality water conjunctively with fresh water resources in the water-limited areas.

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Section: Discussionmentioning

confidence: 99%

Predicting Irrigation Water Quality Indices Based on Data-Driven Algorithms: Case Study in Semiarid Environment

Dimple

Rajput

Al‐Ansari

et al. 2022

Journal of Chemistry

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“…However, assessing groundwater quality using GWQI is time-consuming and costly (Tung et al 2020). To overcome the limitations of GWQI, some researchers have turned to non-physical methods using artificial intelligence (AI) models (Imneisi 2019;Kadam et al 2019;Gaya et al 2020;Agrawal et al 2021;Asadollah et al 2021;Elbeltagi et al 2021). This approach is based on the idea that any system can learn from datasets, create models, and then make decisions with the least amount of manual intervention (Azrour et al 2022).…”

Section: Introductionmentioning

confidence: 99%

Modeling of groundwater quality index by using artificial intelligence algorithms in northern Khartoum State, Sudan

Mohammed

Khleel

Szabó

et al. 2022

Model. Earth Syst. Environ.

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In the present study, multilayer perceptron (MLP) neural network and support vector regression (SVR) models were developed to assess the suitability of groundwater for drinking purposes in the northern Khartoum area, Sudan. The groundwater quality was evaluated by predicting the groundwater quality index (GWQI). GWQI is a statistical model that uses sub-indices and accumulation functions to reduce the dimensionality of groundwater quality data. In the first stage, GWQI was calculated using 11 physiochemical parameters collected from 20 groundwater wells. These parameters include pH, EC, TDS, TH, Cl−, SO4−2, NO3−, Ca+2, Mg+2, Na+, and HCO3−. The primary investigation confirmed that all parameters except for EC and NO3− are beyond the standard limits of the World Health Organization (WHO). The measured GWQI ranged from 21 to 396. As a result, groundwater samples were classified into three classes. The majority of the samples, roughly 75%, projected into the excellent water category; 20% were considered good water and 5% were classified as unsuitable. GWQI models are powerful tools in groundwater quality assessment; however, the computation is lengthy, time-consuming, and often associated with calculation errors. To overcome these limitations, this study applied artificial intelligence (AI) techniques to develop a reliable model for the prediction of GWQI by employing MLP neural network and SVR models. In this stage, the input data were the detected physiochemical parameters, and the output was the computed GWQI. The dataset was divided into two groups with a ratio of 80% to 20% for models training and validation. The predicted (AI) and actual (calculated GWQI) models were compared using four statistical criteria, namely, mean square error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). Based on the obtained values of the performance measures, the results revealed the robustness and efficiency of MLP and SVR models in modeling GWQI. Consequently, groundwater quality in the north Khartoum area is evaluated as suitable for human consumption except for BH 18, where highly mineralized water is observed. The developed approach is advantageous in groundwater quality evaluation and is recommended to be incorporated in groundwater quality modeling.

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“…Furthermore, the interpretation of the conventional WQI models is built on the overall categorization of the groundwater samples without considering the processes and parameters controlling the groundwater. To overcome the limitations of WQI estimates, some researchers have turned to a non-physical technique that has been successful in forecasting WQI (Agrawal et al, 2021;Asadollah et al, 2021;Elbeltagi et al, 2021;Gaya et al, 2020;Imneisi, 2019;Kadam et al, 2019) and other water quality parameters (Chidambaram et al, 2022;El Bilali et al, 2021;Shiri et al, 2021;Yesilnacar & Sahinkaya, 2012) using machine learning (ML) artificial intelligence (AI) models. As a result, implementing a cost-effective strategy for reliable water quality evaluation is critical.…”

Section: Introductionmentioning

confidence: 99%

Development of Artificial intelligence model with aid of statistical methods for simulation of water quality indices in north Khartoum area, Sudan

Mohammed

Khleel

Szabó

et al. 2022

Preprint

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Groundwater quality evaluation is among the most critical aspects of water management concepts. As a result of overexploitation due to overpopulation, groundwater is severely deteriorating. Water Quality Index (WQI) is commonly used to appraise groundwater quality. In the present study, WQI model is developed to evaluate the groundwater quality in north Khartoum area. To classify water quality for diverse reasons, WQI uses multiple asset classes, sub-indices, and an accumulation function. Multivariate statistical analyses were applied to test the correlation between different variables to draw on the main variables and processes affecting the groundwater quality in the study area. Furthermore, correlation analysis (CA) and principal component analysis (PCA) served as guide for weights assignment in WQI calculations and interpretation. The innovative statistically guided WQI models (SWQI) proved to be superior in groundwater quality assessment. Although SWQI models is powerful and effective tool in groundwater assessment, however, conventional computation is lengthy, time consuming and is often observed with enormous errors during the calculation of sub-indices. In this study, artificial intelligence techniques are applied to cope with these limitations. Multilayer perceptron (MLP) and support vector regression (SVR) models were used for prediction of WQI. The dataset was divided into two groups (ratio 80:20) for training and validation respectively. The predicted models were compared with actual models using four statistical criteria, namely mean square error (MSE), root mean squared Error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The obtained results revealed the robustness of the artificial intelligence in prediction of WQI in north Khartoum area. The developed approaches in this study shown to be advantageous in groundwater quality assessment.

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Exploring Artificial Intelligence Techniques for Groundwater Quality Assessment

Cited by 48 publications

References 54 publications

Predicting Irrigation Water Quality Indices Based on Data-Driven Algorithms: Case Study in Semiarid Environment

Predicting Irrigation Water Quality Indices Based on Data-Driven Algorithms: Case Study in Semiarid Environment

Modeling of groundwater quality index by using artificial intelligence algorithms in northern Khartoum State, Sudan

Development of Artificial intelligence model with aid of statistical methods for simulation of water quality indices in north Khartoum area, Sudan

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