October – December Integrating Machine Learning-Based Soil Erosion Susceptibility Mapping with Ecosystem Restoration and Climate Adaptation Planning in the Ugam–Chatkal Mountain Ecosystem, Uzbekistan.
Received: 2026-10-09
Published: 2026-10-12
Abstract
Identifying areas susceptible to soil erosion can support ecosystem protection, but restora-
tion planning also requires evidence of existing degradation and local ecological conditions. This
paper proposes a methodological framework linking machine learning-based soil erosion suscepti-
bility mapping with ecosystem restoration and climate adaptation planning in the Ugam–Chatkal
mountain ecosystem, Uzbekistan. The framework integrates geographic information systems, remote
sensing, terrain variables, soil laboratory measurements, erosion observations, and climate informa-
tion. It comprises spatial data preparation, model development, spatial validation, and the assessment
of management priorities. Susceptibility maps are considered alongside vegetation condition and
field evidence to distinguish areas requiring preventive protection, active restoration, or additional
investigation. Climate adaptation is addressed through the proposed assessment of historical climate
variability and future scenarios, with attention to uncertainty and model transferability. Two con-
ceptual diagrams illustrate the research workflow and management decision framework. The paper
presents a proposed approach rather than completed modelling results. Its intended contribution is a
transparent basis for selecting restoration priorities and designing subsequent field assessment and
monitoring.
Keywords
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