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- W3204154422 abstract "The instability of soil aggregates is one of the most important land degradation processes in different parts of Iran, particularly in arid and semi-arid regions with loess-derived soils. In this study, we modeled soil stability (measured as soil bulk density (BD) of the topsoil layer) using two algorithms: random forest (RF) and support vector regression (SVR). In this study, we used nine environmental variables, driven by unmanned aerial vehicles (UAV) and soil samples collected from a depth of 0–20 cm to map BD. The RF algorithm was applied to prioritize the factors affecting the BD. The Results were assessed using RMSE, MSE, and R2. The results of the variable importance analysis showed that distance to a river, distance to road, and altitude are the most important factors affecting soil stability. The prediction model for mapping soil BD has reasonable accuracy, i.e., the RMSE values of the training set of RF and SVR models were 0.08 (R2 = 0.95) and 0.16 (R2 = 0.21), respectively. We recognized that the methodology of this study could easily produce a soil property probability map (BD in this study) in the topsoil layer." @default.
- W3204154422 created "2021-10-11" @default.
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- W3204154422 date "2022-01-01" @default.
- W3204154422 modified "2023-10-18" @default.
- W3204154422 title "Digital soil mapping of soil bulk density in loess derived-soils with complex topography" @default.
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- W3204154422 doi "https://doi.org/10.1016/b978-0-323-89861-4.00018-x" @default.
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