Machine Learning Techniques for Gully Erosion Susceptibility Mapping (Case Study: Mukhtaran Watershed, South Khorasan Province, Iran) | ||
| Sustainable Earth Trends | ||
| دوره 5، شماره 3، 2025، صفحه 63-75 اصل مقاله (2.07 M) | ||
| نوع مقاله: Original Research Article | ||
| شناسه دیجیتال (DOI): 10.48308/set.2025.237956.1093 | ||
| نویسندگان | ||
| Javad Momeni Damaneh1؛ Hadi Memarian* 2؛ Ali akbar Safdari3 | ||
| 1Department of Natural Resources Engineering, Agriculture and Natural Resources Faculty, Hormozgan University, Bandarabbas, Iran | ||
| 2Department of Watershed Management, Faculty of Natural Resources and Environment, University of Birjand, Birjand, Iran | ||
| 3Department of Natural Resources Engineering, Natural Resources Faculty, Tehran University, Tehran, Iran | ||
| چکیده | ||
| Gully erosion, a significant environmental issue, can lead to severe consequences like soil loss, habitat destruction, and water pollution. To mitigate its impact, accurate mapping of land sensitivity to gully erosion is crucial. Machine learning models offer a powerful approach to predict and map gully erosion susceptibility. This study focuses on the Mukhtaran basin in South Khorasan province, Iran. By employing various machine learning techniques, including GLM, GBM, CTA, ANN, SRE, FDA, MARS, RF, and MaxEnt, the researchers aimed to identify the most suitable model for predicting gully erosion. Twenty-two environmental factors were selected and analyzed, with a focus on physiographic, climatic, hydrological, soil, land surface/cover, and geological variables. The results showed that the random forest (RF) and ensemble (ESMs) models demonstrated the highest accuracy in predicting gully erosion susceptibility, with a TSS index of 0.97. Sensitivity analysis revealed that the digital elevation model, soil electrical conductivity, bare soil percentage, land unit components, geology, runoff coefficient, and maximum storage capacity were the most influential factors. The study emphasizes the potential of machine learning models in generating accurate gully erosion susceptibility maps. However, further research is needed to explore additional factors and improve data quality. By combining topographic/hydrologic indices with machine learning models, more precise estimates of gully paths can be obtained for use in process-based models. | ||
| کلیدواژهها | ||
| Arid watershed؛ ML performance؛ Random forest؛ Spatial prediction؛ Vulnerability assessment | ||
| مراجع | ||
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