Machine learning approach for water quality predictions based on multispectral satellite imageries

Water quality analysis is a vital component of the water resources management and has to be undertaken promptly to make sure environmental regulations are being followed and to eliminate any pollution that could harm the ecosystem. The main objective of this study to retrieve and map the water quality parameters from Sentinel-2 and ResourceSat-2 [Linear Imaging Self-Scanning Sensor (LISS)–IV] multi-spectral satellite data, using Support Vector Machines (SVM), Random Forests (RF), and Multi-Linear regression (MLR) models. This study represents the first attempt to demonstrate the applicability and performance of high-spatial resolution ResourceSat-2 remote sensing satellite's LISS-4 sensor, which operates in three spectral bands in the Visible and Near Infrared Region (VNIR), to predict water quality. Spectral bands of each satellite were used as independent parameter to generate the algorithms for pH, Dissolved Oxygen (DO), Total Suspended Solids (TSS) and Total Dissolved Solids (TDS). The model performance was evaluated based on coefficient of determination (R²), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the Root Mean Square Error (RMSE) statistical indices. The result of this study indicates that the SVM yielded the highest accuracy followed by the RF and MLR. The R², MAE, MAPE and RMSE ranged between 0.78 and 0.99, 0.049–0.24, 0.01–10.9 % and 0.05–0.28 respectively for all the four SVM models across both the sensors. Based on the spatial trend Sentinel-2 was found to be slightly superior to the ResourceSat-2 (LISS-IV) for the estimation of water quality parameters owing to its superior spectral and radiometric resolution, nevertheless ResourceSat-2 (LISS-IV) has its own advantage in terms of high spatial resolution. The results of this study highlight the high potential of machine learning models in conjunction with multispectral satellite images to manage water quality.