Sub-watershed prioritization using morphometric analysis, principal component analysis, hypsometric analysis, land use/land cover analysis, and machine learning approaches in the Peddavagu River Basin, India

Abstract Water resource management is critical in the face of climate change to reduce water scarcity and meet demands an expanding population. Prioritization watersheds has gained significance natural management, particularly context watershed management. This study prioritizes sub-watersheds for Peddavagu basin using five methods. The four methods mentioned above can be estimated utilizing remote sensing (RS) geographic information system (GIS) approaches, while linear discriminant analysis (LDA) machine learning techniques. catchment resulted formation 13 sub-watersheds. quantitative measurements morphometric analysis, including linear, relief, areal, were considered, 18 characteristics chosen rank prioritize Principal component (PCA) was used based on highly correlated parameters. land use/land cover (LULC) features LDA two Using hypsometric integral (HI) values, prioritization been done. Sub-watersheds prioritized. Based methods, classified as low, medium, high. Among identified high priority, immediate priority assigned SW10. Decision-makers research region use findings plan implement