Application of Support Vector Machine (SVM) and Boosted Regression Tree (BRT) to Model the Sensitivity of Gully Erosion in the Watershed of Shore River Moher City
The aim of this study is to develop sensitive gully erosion models by implementing a machine learning algorithm (Support Vector Machine and Boosted Regression Tree) in the Moher basin. First, gully areas are identified, and then 13 variables predisposing to gully erosion (Slope, Slope Direction, Topographic Wetness Index, Streem Power Index, Terrain Ruggedness Index, Distance from Waterway, Drainage Density, Distance from Road, Land use, NDVI, Avera annual Rainfall, Geology, and Soil Texture) were selected. The variance inflation coefficient was used to evaluate multicollinearity between variables. Finally, a gully erosion sensitivity map was prepared in the environment (R). Also, the effect of physical and chemical characteristics of soil on gully erosion was investigated using Multivariate Regression. Regarding the importance of variables, Geology has the most significant effect on gully erosion in the SVM model, Land use, and the BRT model. The predicted sensitivity map was validated with the help of the receiver operating characteristic (ROC) curve. The results showed that the area under the curve (AUC) in the Support Vector Machine and Boosted Regression Tree models were calculated as 0.92 and 0.94, respectively, which led to accurate prediction. Also, the results showed that the sand variable (9.299), sodium absorption ratio (7.967), and TNV (6.185) have the most significant effect on gully erosion
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