Predictive Analytics for Identifying Surface Water Sources for Domestic Water Supply in Phuentsholing, Bhutan
Year: 2025
Type: article
Abstract: Surface water is a primary source of drinking water in Bhutan. To ensure a reliable supply thatmeets both quality and quantity requirements, it is crucial to identify suitable sources. This studypresents an integrated approach using Geographic Information Systems (GIS) and machine learningto identify potential surface water sources. Satellite imagery from Landsat-8 and Sentinel-2 wasutilized to generate geospatial datasets. Five key variables influencing the spatio-temporal presenceof water—rainfall, temperature, soil type, Normalized Difference Vegetation Index (NDVI), andtopography were analyzed within a GIS environment. The Random Forest (RF) algorithm, knownfor its robustness in handling nonlinear and high-dimensional data, was employed to predictpotential water sources. Model outputs were validated through field surveys and spectral analysisusing the Normalized Difference Water Index (NDWI). The study identified 50 viable water sourcelocations situated above 450 meters in elevation. The model achieved an area under the curve (AUC)score of 0.99, indicating a strong correlation between predicted and actual water sources. Theseresults confirm that integrating machine learning with remote sensing and GIS is an effectiveapproach for surface water resource planning in Bhutan's hilly terrain.
Source: Zorig Melong.
DOI: 10.17102/zmv8.i2.026
Institutions: College of Science and Technology, Royal University of Bhutan
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4413441995