Landslide risk zoning using support vector machine algorithm

• Main Authors: Ghiasi V., Pauzi N.I.M., Karimi S., Yousefi M.
• Other Authors: 26535838400
• Format: Article
• Published: Techno-Press 2024
• Subjects: kernel functions; landslide; logistic function; mapping; prediction rate-area diagram; support vector machine; Disasters; Losses; Risk assessment; Support vector machines; Topography; Vectors; Watersheds; Financial loss; Gaussian kernels; Kernel function; Landslide risk zoning; Logistics functions; Natural disasters; Prediction rate-area diagram; Prediction-rates; Support vector machines algorithms; Support vectors machine; Landslides

Landslides are one of the most dangerous phenomena and natural disasters. Landslides cause many human and financial losses in most parts of the world, especially in mountainous areas. Due to the climatic conditions and topography, people in the northern and western regions of Iran live with the risk of landslides. One of the measures that can effectively reduce the possible risks of landslides and their crisis management is to identify potential areas prone to landslides through multi-criteria modeling approach. This research aims to model landslide potential area in the Oshvand watershed using a support vector machine algorithm. For this purpose, evidence maps of seven effective factors in the occurrence of landslides namely slope, slope direction, height, distance from the fault, the density of waterways, rainfall, and geology, were prepared. The maps were generated and weighted using the continuous fuzzification method and logistic functions, resulting values in zero and one range as weights. The weighted maps were then combined using the support vector machine algorithm. For the training and testing of the machine, 81 slippery ground points and 81 non-sliding points were used. Modeling procedure was done using four linear, polynomial, Gaussian, and sigmoid kernels. The efficiency of each model was compared using the area under the receiver operating characteristic curve