Development and prediction of a robust multivariate trophic state index for the classification of lentic water bodies

Abstract

This study presents a novel parametric-non parametric composite approach for developing Multivarite Trophic State Index (MTSI) for the classification of lentic water bodies (LWB). The newly developed framework involves the use of water quality parameters (WQPs), Principal Component Analysis (PCA) and Non-Parametric Gaussian Kernal Density (NPGKD) estimator. The generic framework is demonstrated for developing MTSI, based on the datasets of 18 LWBs in Kerala, India. The method could capture the hypereutrophic status and Akkulam lake was found to be in this state and three other lakes are in eutrophic state for the 2012–2018 database used in this study. The method of developing MTSI is generic and with discrete threshold values which enable its application to any hydroclimatic region. Subsequently, a novel Machine Learning based framework is proposed for prediction of MTSI considering WQPs as inputs. Four machine learning methods and the linear regression are applied for the prediction of MTSI of LWBs of Kerala, considering six combinations of input datasets of WQPs. The rigorous performance evaluation based on multitude of error, coefficient and graphical measures confirmed the superiority of Random Forest as the best method for the prediction of MTSI, for the chosen database. The combination of nine WQPs involving both physical and chemical parameters is found to be the best for MTSI prediction and machine learning offers flexibility to get comparable performance even with lesser number of input variables.