Machine learning–based prioritization of sub-watersheds for soil erosion management: A case study of the Bardha watershed
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Date
2026-03-26
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Publisher
Elsevier Inc
Abstract
Soil erosion is a major environmental concern that affects land productivity and water quality. Although soil
erosion is a serious global environmental challenge, understanding its potential influence in the Bardha
Watershed is important due to its topographical characteristics and the dependence of local communities on land
resources for agriculture. Morphometric analysis helps assess a watershed's physical characteristics to understand
its erosion potential. In this study, sub-watersheds were delineated using the shuttle radar topography mission
(SRTM) digital elevation model (DEM) to accurately derive drainage and terrain characteristics. To enhance the
precision of sub-watershed prioritization, morphometric analysis is combined with multi-criteria decisionmaking (MCDM) techniques. This research ranks sub-watersheds in the Bardha watershed in Chhattisgarh using
morphometric parameters in combination with four MCDM approaches: additive ratio assessment (ARAS), multiobjective optimization by ratio analysis (MOORA), visekriterijumsko kompromisno rangiranje (VIKOR) and
simple additive weighting (SAW). The criteria weights for these MCDM methods are determined using the
criteria importance through intercriteria correlation (CRITIC) method. Furthermore, the novelty of this study lies
in the integration of machine learning (ML) techniques, specifically support vector machine (SVM) and random
forest (RF). By combining the outputs of all six methods, the study developed a unified priority map, which was
subsequently classified into high, medium, and low priority zones. The study found that sub-watershed 3 (SW3)
and SW4 fall into the common high-priority category; SW2, SW6, and SW7 into the medium category; and SW1
and SW5 into the low-priority group. This integrated method makes decision-making stronger by letting planners
focus on high-priority sub-watersheds for strategic development, conservation, and optimal land management.
This study aligns with SDG 15 by addressing land degradation through the identification and management of soil
erosion-prone areas.