Driving Sustainable Water Solutions: Optimizing Water Quality Prediction using a Stacked Hybrid Model with Gradient Boosting and Ridge Regression

The Sustainable Development Goal 6, which aims to guarantee the availability and sustainable management of water and sanitation for all,Is held in high respect. However, in many regions of the world, deteriorating water quality is a serious worldwide issue that appears to be outpacing the development of environmental policies as a result of rising human populations, expanding industrial and agricultural sectors, and the threat posed by climate change. This research addresses the problem of water quality assessment by utilizing advanced regression techniques for predicting the Water Quality Index (WQI) score, a key indicator which reflects overall effect of various pollutants present in water bodies. This study presents a novel approach of integrating Gradient Boosting and Ridge Regression through a Stacking Regressor, using environmental data to predict WQI. It is evaluated using mean squared error (MSE) and R-squared metrics, demonstrating consistency and robustness in WQI prediction. In contrast to the traditional methods, our approach incorporates sophisticated ensemble methods and optimized feature engineering approaches to address the problem of dataset imbalance and to learn complex non-linear dependencies among different water quality variables. As a result, the approach significantly enhances the accuracy of water quality evaluation, which is key in ensuring proper management of water resources, as dictated by SDG 6. This research contributes to aiding in effective water resource management and rapid decision-making to ensure water safety.