Head-cut gully erosion susceptibility modelling based on ensemble Random Forest with oblique decision trees in Fareghan watershed, Iran

Abstract

Gully erosion is the most active hydro-geomorphological phenomenon in the continental areas due to the high erosion rates triggered by the gully system. Monitoring and modelling gully development and gully distribution will contribute to understand landforms evolution and risk assessment. The purpose of the current research is to model head-cut gully erosion susceptibility (HCGES) using support vector machine (SVM), random forest (RF) and novel ensemble model of random forest with four Oblique methods (Logistic Regression, Ridge Regression, Partial least squares (PLS) and Support vector machine (SVM)) (hereafter called ensemble ORF) data mining models in Fareghan watershed, Hormozghan province, Iran. For this purpose, 14 variables influencing the gully development, were prepared and 145 head-cut gully erosion locations were identified in the study area. The efficiency of SVM, RF, ensemble ORF were evaluated based on receiver operating characteristic (ROC), the results have shown that all these three models are highly accurate and robust in predicting the head-cut gully erosion susceptibility zones. The results of the models were evaluated based on the area under the receiver operatic characteristic curve (AUC) in the validation stage presented that the efficiency of these models are 0.91, 0.94, and 0.96, respectively. Altitude and distance from the road in all three models were more important than other variables. The findings of this research will contribute to develop gully control strategies and to prevent the gully initiation where gully erosion is more susceptible.