A landslide susceptibility map based on spatial scale segmentation: A case study at Zigui-Badong in the Three Gorges Reservoir Area, China

Abstract

China experiences frequent landslides, and therefore there is a need for landslide susceptibility maps (LSMs) to effectively analyze and predict regional landslides. However, the traditional methods of producing an LSM are unable to account for different spatial scales, resulting in spatial imbalances. In this study, Zigui-Badong in the Three Gorges Reservoir Area was used as a case study, and data was obtained from remote sensing images, digital elevation model, geological and topographic maps, and landslide surveys. A geographic weighted regression (GWR) was applied to segment the study area into different spatial scales, with three basic principles followed when the GWR model was applied for this propose. As a result, 58 environmental factors were extracted, and 18 factors were selected as LSM factors. Three of the most important factors (channel network basic level, elevation, and distance to river) were used as segmentation factors to segment the study area into 18 prediction regions. The particle swarm optimization (PSO) algorithm was used to optimize the parameters of a support vector machine (SVM) model for each prediction region. All of the prediction regions were merged to construct a GWR-PSO-SVM coupled model and finally, an LSM of the study area was produced. To verify the effectiveness of the proposed method, the outcomes of the GWR-PSO-SVM coupled model and the PSO-SVM coupled model were compared using three evaluation methods: specific category accuracy analysis, overall prediction accuracy analysis, and area under the curve analysis. The results for the GWR-PSO-SVM coupled model for these three evaluation methods were 85.75%, 87.86%, and 0.965, respectively, while the results for the traditional PSO-SVM coupled model were 68.35%, 84.44%, and 0.944, respectively. The method proposed in this study based on a spatial scale segmentation therefore acquired good results.

Evaluation of Shannon Entropy and Weights of Evidence Models in Landslide Susceptibility Mapping for the Pithoragarh District of Uttarakhand State, India

“…e SFM is the most sensitive component of the slope relative to the landslide in the area studied by "Cherty Quartzit, Dolomite with Epidiorite Dykes", this type of material contributing to the highest number of landslide (67) and also the highest weight landslide (C � 1.45) and density landslide (P ij � 0.35) in accordance with the WOE and SE methods, respectively. e SFM is an another unique feature of each region and offers different results in different regions.…”

“…(2) Aspect. e slope aspect is considered as an important parameter in assessing landslide susceptibility [66] as the sun, air/wind and rain/precipitation of the region affect in different directions [67]. On the other hand, aspect indirectly affects the vegetation and soil moisture.…”

A Comparative Study of Landslide Susceptibility Mapping Using SVM and PSO-SVM Models Based on Grid and Slope Units

“…Each particle is adjusted according to the fitness values of themselves and the swarm. And the iteration and optimization are not terminated until all the particles converge to optimal solution [68][69][70][71] (Figure 5(b)). Hence, particles are optimized by constantly updating their speed and position, in which process can be expressed as…”

GIS-Based Soft Computing Models for Landslide Susceptibility Mapping: A Case Study of Pithoragarh District, Uttarakhand State, India

“…(2) Slope Aspect. Slope aspect is also deemed as a significant parameter to effectively evaluate the landslide susceptibility that is associated with the direction of the sun, air/wind, and rain/precipitation in the specified area [57,58]. is factor also indirectly impacts the vegetation and amount of water held in the soil.…”