Performance assessment of the landslide susceptibility modelling using the support vector machine, radial basis function network, and weight of evidence models in the N'fis river basin, Morocco

Abstract

Landslides in mountainous areas are one of the most important natural hazards and potentially cause severe damage and loss of human life. In order to reduce this damage, it is essential to determine the potentially vulnerable sites. The objective of this study was to produce a landslide vulnerability map using the weight of evidence method (WoE), Radial Basis Function Network (RBFN), and Support Vector Machine (SVM) for the N'fis basin located on the northern border of the Marrakech High Atlas, a mountainous area prone to landslides. Firstly, an inventory of historical landslides was carried out based on the interpretation of satellite images and field surveys. A total of 156 historical landslide events were mapped in the study area. 70% of the data from this inventory (110 events) was used for model training and the remaining 30% (46 events) for model validation. Next, fourteen thematic maps of landslide causative factors, including lithology, slope, elevation, profile curvature, slope aspect, distance to rivers, topographic moisture index (TWI), topographic position index (TPI), distance to faults, distance to roads, normalized difference vegetation index (NDVI), precipitation, land use/land cover (LULC), and soil type, were determined and created using the available spatial database. Finally, landslide susceptibility maps of the N'fis basin were produced using the three models: WoE, RBFN, and SVM. The results were validated using several statistical indices and a receiver operating characteristic curve. The AUC values for the SVM, RBFN, and WoE models were 94.37%, 93.68%, and 83.72%, respectively. Hence, we can conclude that the SVM and RBFN models have better predictive capabilities than the WoE model. The obtained susceptibility maps could be helpful to the local decision-makers for LULC planning and risk mitigation.

Integrating Machine Learning Ensembles for Landslide Susceptibility Mapping in Northern Pakistan

“…In this regard, along with compulsory information, the utilization of cutting-edge mathematical models also remains pivotal to obtaining precise results. The prediction of landslide risk entails the application of different models, for instance, weight of evidence (WOE) [11], frequency ratio (FR) [12], the analytic hierarchy process (AHP) [13], and fuzzy logic (FL) [14], which are among the most elementary and widely used approaches. Contemporarily, these approaches are considered conventional and are being replaced by machine learning (ML) models.…”

A comparative evaluation of landslide susceptibility mapping using machine learning-based methods in Bogor area of Indonesia

“…Among the globally implemented approaches in generating LSM, the Geographic Information System (GIS) and remote sensing approaches have been notably developing (Huang et al, 2020;Naceur et al, 2022;Zhang et al, 2022;Khaliq et al, 2023). In general, there are four types of LSM approach: physical-based models, knowledgebased models (i.e.…”

Landslide susceptibility modeling and mapping at Dien Bien province, Vietnam using Bagging based MLP neural network

“…Out of these, models based on machine learning (ML) are recently wellknown as the highest advanced statistical based models for better modeling and mapping of landslide susceptibility [6]. Popular ML models used for landslide susceptibility modeling are Support Vector Machines [7,8], Artificial Neural Networks -ANN [9,10], Decision Trees [11,12], and Random Forests -RF [13,14]. More recently, hybrid/ensemble models which are combinations of single ML models and different optimization techniques are considered as better tools compared with single ML models for landslide susceptibility modeling [15].…”