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 R-2, i.e., R-2 = 0996 against the RF (R-2 = 0.886), XGBoost (R-2 = 0.0.927), and ANN (R-2 = 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. (C) 2021 Elsevier Ltd. All rights reserved.

Automated summary

To ensure safe drinking water in the future, understanding the quality and pollution level of existing groundwater is crucial, with high-accuracy water quality prediction being key. The deep learning model showed the highest accuracy in predicting groundwater quality, making it the most realistic and accurate approach for such predictions.