Authors: M.Gokul, K.Jeevalakshmi
Abstract: Predictive modeling of the parameters affecting water quality is crucial for public health, agricultural sustainability, and proper environmental action in advance. Existing lab-based monitoring techniques, being reliable and accurate, are not only expensive but also limited in terms of spatio-temporal coverage, which reduces their practical applicability. This research work aims to create an effective machine learning model for WQI and WQC prediction using the set of standard physicochemical water parameters. In particular, the methodology incorporates pre-processing using mean imputation and min-max normalization, feature importance analysis, and ensembles of several ML models, including Random Forest, XGBoost, Gradient Boosting, and Multi-layer Perceptron. As demonstrated by experiments, ensembles of tree-based algorithms are generally more accurate than individual baseline approaches in terms of prediction reliability for various types of water bodies. The paper also explores feature dominance and shows that pH, dissolved oxygen, and turbidity have the strongest effect on water quality state.
DOI: https://doi.org/10.5281/zenodo.23557991
