期刊
HYDROLOGY
卷 10, 期 1, 页码 -出版社
MDPI
DOI: 10.3390/hydrology10010007
关键词
soil erosion; sub-watersheds; machine learning; burned areas; protected areas
Protected areas are crucial in mitigating soil erosion. This study evaluated the performance of machine learning models, namely Support Vector Machine (SVM) and Random Forest (RF), in identifying indicators of soil erosion in sub-watersheds and protected areas in northern Portugal. The analysis considered various parameters, such as soil erosion data, burned area, and morphometric factors. The findings showed that morphometric factors, soil erosion data, and burned area play significant roles in analyzing soil erosion in watersheds.
Protected areas (PA) play an important role in minimizing the effects of soil erosion in watersheds. This study evaluated the performance of machine learning models, specifically support vector machine with linear kernel (SVMLinear), support vector machine with polynomial kernel (SVMPoly), and random forest (RF), on identifying indicators of soil erosion in 761 sub-watersheds and PA in northern Portugal, by using soil erosion by water in Europe, according to the revised universal soil loss equation (RUSLE2015), as target variable. The parameters analyzed were: soil erosion by water in Europe according to the revised universal soil loss equation (RUSLE2015), total burned area of the sub-watershed in the period of 1975-2020, fire recurrence, topographic wetness index (TWI), and the morphometric factors, namely area (A), perimeter (P), length (L), width (W), orientation (O), elongation ratio (Re), circularity ratio (Rc), compactness coefficient (Cc), form factor (Ff), shape factor (Sf), DEM, slope, and curvature. The median coefficient of determination (R-2) for each model was RF (0.61), SVMpoly (0.68), and SVMLinear (0.54). Regarding the analyzed parameters, those that registered the greatest importance were A, P, L, W, curvature, and burned area, indicating that an analysis which considers morphometric factors, together with soil erosion data affected by water and soil moisture, is an important indicator in the analysis of soil erosion in watersheds.
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