A novel integrated approach to predict the sodium absorption ratio (SAR) of groundwater sustainability using deep learning models and SHAP approach

Abstract

Agriculture is a crucial factor in improving the country economic growth. With the massive impact of environment variation, supplying and measuring the quality of water for irrigation usage is a crucial task for water resource management authority. The demand for good quality water for both drinking and irrigation uses is increasing day by day in recent years. The main aim of the present study is to identify the suitability of surface water for irrigation uses in the Man River basin of Maharashtra, India using advanced techniques. These region first time is used such kind of models i.e. boosted tree, AdaBoost, decision tree, extremely randomized tree model, and feed-forward neural network (deep learning) models, and these models are better to analyze and understand the groundwater quality datasets in the saline area. The prediction results are evaluated with the use of different performance metrics, mean absolute error (MAE), mean absolute relative error (MARE), Nash Sutcliffe efficiency (NSE), root mean squared error (RMSE), and coefficient of determination (R²). The study identified that the boosted tree model is more appropriate for the SAR prediction value, this model with high accuracy compared with other models. The result of this study shows that boosted tree model is very suitable for prediction of SAR and also provided the accurate information for agriculture purposes. In the first scenario, the boosted tree model shows lower value of mean squared error (MSE) of 0.26 and higher R² of 0.88, the second scenario shows lower value of MSE of 0.11 and higher value of R² of 0.91. Overall, in both the scenarios, the result of boosted tree model is more favorable for SAR% prediction. This work shows that machine and deep learning models can improve and better prediction of the groundwater quality in the study area. It is essential to understand the water quality and improve the sustainable agriculture system and development. These advanced modeling methods help to stakeholders make better water management and irrigation decisions, boosting agricultural sustainability and productivity.