Performance evaluation of different machine learning algorithms for prediction of nitrate in groundwater in Thiruvannamalai District

Abstract

The prevalence of nitrates (NO3−) in groundwater due to the extensive application of fertilizers and anthropogenic sources pollutes the groundwater. Machine learning (ML) techniques are now being increasingly deployed to achieve high precision in predicting water quality. This study assesses the efficacy of nine distinct ML algorithms, namely, linear regression, polynomial regression, decision tree, random forest (RF), support vector machine, multilayer perceptron regressor, eXtreme gradient boosting (XGB), light gradient boosting (LGB), and K‐nearest neighbors to predict nitrate concentration in the groundwater in Thiruvannamalai District, Tamil Nadu. Overall, 360 water samples for 1 year and 14 groundwater variables were determined to predict nitrate. Performance evaluation metrics such as root mean square error (RMSE), moving average error (MAE), and correlation coefficient (R2) were evaluated for pre‐monsoon, monsoon, and post‐monsoon seasons. For all three seasons, RF predicted the nitrate concentration with low values of RMSE, MAE, and higher values of R2. The results show values for RF with: RSME: 0.49, MAE: 1.30, and R2: 0.94, which has a higher prediction tailed by LGB and XGB and is true for all the seasons. The results from the study will aid the policymakers in planning the strategy for remediation.