Gully erosion has been identified in recent decades as a global threat to people and
property. This problem also affects the socioeconomic stability of societies and
therefore limits their sustainable development, as it impacts a nonrenewable
resource on a human scale, namely, soil. The focus of this study is to evaluate the
prediction performance of four machine learning (ML) models: Logistic Regression
(LR), classification and regression tree (CART), Linear Discriminate Analysis (LDA), and
the k-Nearest Neighbors (kNN), which are novel approaches in gully erosion modeling
research, particularly in semi-arid regions with a mountainous character. 204 samples
of erosion areas and 204 samples of non-erosion areas were collected through field
surveys and high-resolution satellite images, and 17 significant factors were
considered. The dataset cells of samples (70% for training and 30% for testing)
were randomly prepared to assess the robustness of the different models. The
functional relevance between soil erosion and effective factors was computed
using the ML models. The ML models were evaluated using different metrics,
including accuracy, the kappa coefficient. kNN is the ideal model for this study.
The value of the AUC from ROC considering the testing datasets of KNN is 0.93; the
remaining models are associated to ideal AUC and are similar to kNN in terms of
values. The AUC values from ROC of GLM, LDA, and CART for testing datasets are
0.90, 0.91, and 0.84, respectively. The value of accuracy considering the validation
datasets of LDA, CART, KNN, and GLM are 0.85, 0.82, 0.89, 0.84 respectively. The
values of Kappa of LDA, CART, and GLM for testing datasets are 0.70, 0.65, and 0.68,
respectively. ML models, in particular KNN, GLM, and LDA, have achieved outstanding
results in terms of creating soil erosion susceptibility maps. The maps created with the
most reliable models could be a useful tool for sustainable management, watershed
conservation and prevention of soil and water losses.info:eu-repo/semantics/publishedVersio
