PREDICTION OF GROUNDWATER LEVEL FLUCTUATIONS USING REMOTE SENSING INPUTS AND RANDOM FOREST MODELING: A CASE STUDY FROM THE INDUS BASIN
DOI:
https://doi.org/10.71146/kjmr948Keywords:
groundwater prediction, random forest, remote sensing, Indus Basin, machine learning, NDVI, groundwater fluctuation, water managementAbstract
Accurate prediction of groundwater level fluctuations is essential for effective water resource planning in regions facing over-extraction and climate-induced stress. This study employs a machine learning approach, the Random Forest (RF) algorithm, integrated with remote sensing-derived environmental indicators to predict groundwater level variations across the Indus Basin, Pakistan. Key predictor variables, including precipitation, land surface temperature (LST), vegetation indices (NDVI), evapotranspiration, and land use changes, were extracted from MODIS, TRMM, and Landsat datasets. The model was trained and validated using long-term groundwater observation data (2005–2022) obtained from the Pakistan Council of Research in Water Resources (PCRWR). The RF model achieved high accuracy (R² = 0.89, RMSE = 0.47 m), indicating strong predictive performance. Spatial maps generated from the model reveal significant groundwater decline in intensively irrigated zones of Punjab and Sindh. The integration of remote sensing data and machine learning techniques provides a robust, data-driven framework for groundwater monitoring and sustainable management in large river basins under changing climatic and anthropogenic pressures.
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