Random Forest-Based Soil Mapping to Support Sustainable Watershed Management and SDG 15 (Life on Land): A Case Study of the Curahkemadu Micro Watershed
DOI:
10.29303/jppipa.v12i4.14469Published:
2026-04-25Downloads
Abstract
Soil characteristics in the Curahkemadu Micro Watershed are influenced by volcanic parent material and other soil-forming factors, including organisms, topography, climate, and time. This study aims to analyze the spatial distribution of soil up to the subgroup level and identify the dominant factors that shape it using a machine learning approach with the Random Forest (RF) algorithm. Field surveys were conducted at 14 Land Mapping Units (LMU) in the Bocek and Donowarih villages, followed by laboratory analysis of the physical and chemical properties of the soil. Soil classification followed the United States Department of Agriculture (2022) system, utilizing Geographic Information Systems (GIS) and ArcGIS Pro for spatial mapping. The classification results showed the existence of two main subgroups: Andic Humudepts (172.73 ha) and Typic Humudepts (577.73 ha). The spatial distribution of soil was strongly influenced by slope, sub-landform, and elevation, with variable importance values of 40%, 35.7%, and 24.3%, respectively. The RF model showed high performance with good accuracy (overall accuracy = 85.7% and Cohen’s Kappa = 0.69). This research provides an important basis for the optimal and sustainable management of watershed areas.
Keywords:
Geographic information system Humudept Soil classification Soil forming TopographReferences
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