Integrated morphometric and machine learning approach for prioritization of erosion-prone sub-watersheds

Abstract

Abstract Soil erosion is a critical global environmental challenge that threatens food security and undermines sustainability efforts worldwide. The accelerating rate of erosion leads to severe land degradation, making it essential to adopt strategic and localized resource management approaches. Prioritizing sub-watersheds allows for more effective conservation planning, as each watershed exhibits unique hydrological and geomorphological characteristics. This study introduces a comprehensive framework for prioritizing sub-watersheds (SWs) in the Aran Basin using an integrated approach. It combines morphometric analysis with multiple criteria decision-making (MCDM) methods—namely VIKOR (visekriterijumsko kompromisno rangiranje), TOPSIS (technique for order preference by similarity to ideal solution), and ARAS (additive ratio assessment)—alongside a machine learning model, support vector machine (SVM), to ensure a more robust and consensus-driven prioritization. The novelty of this research lies in the innovative integration of machine learning with morphometric and MCDM techniques, resulting in a scientifically rigorous and unified ranking of sub-watersheds. Based on the integrated analysis, SW1, SW8, SW9, and SW12 are identified as high-priority sub-watersheds for soil erosion control, collectively covering approximately 30% of the total watershed area, indicating substantial spatial susceptibility. SW2, SW4, SW10, and SW11 fall into the medium-priority category, while SW3, SW5, SW6, and SW7 are classified as low-priority zones. Aligned with sustainable development goal 15 (life on land), this methodology supports informed decision-making for targeted soil conservation and sustainable land management, ultimately contributing to the long-term resilience of terrestrial ecosystems.