Urban and peri-urban lakes are increasingly threatened by water quality degradation due to rising anthropogenic pressures and environmental variability. This study proposes an integrated framework that combines multi-source data and machine learning to estimate and monitor three key water quality parameters: turbidity, total dissolved solids (TDS), and biological oxygen demand (BOD). Field measurements from three lakes in West Bengal, India, Rabindra Sarovar, Mirikh Lake, and Hanuman Ghat Lake, were combined with Landsat-8 satellite imagery, meteorological data, and land use information. Three modeling scenarios were developed: (i) using only remote sensing indices, (ii) combining remote sensing indices with meteorological variables, and (iii) integrating remote sensing indices, meteorological data, and land use features. Principal component analysis (PCA) was used to reduce dimensionality and redundancy. Machine learning models, namely, XGBoost, Decision Tree, and Ridge Regression, were trained and evaluated using R2 and RMSE (Root Mean Square Error) metrics. The third scenario outperformed the others, with Ridge Regression achieving the highest accuracy for BOD prediction (R2 = 0.99). Spatiotemporal patterns revealed persistently high BOD levels along urban lake fringes and post-monsoon spikes in turbidity and TDS, especially in agriculturally influenced zones. These patterns were closely linked to land use practices, rainfall-driven runoff, and point-source pollution. This study underscores the effectiveness of remote sensing and machine learning as scalable tools for real-time water quality monitoring, promoting sustainability through informed lake management strategies in India.
Dawn et al. (Mon,) studied this question.