Landslide susceptibility mapping on global scale using method of logistic regression

Abstract

Abstract. This paper proposes a statistical model for mapping global landslide susceptibility based on logistic regression. After investigating explanatory factors for landslides in the existing literature, five factors were selected to model landslide susceptibility: relative relief, extreme precipitation, lithology, ground motion and soil moisture. When building model, 70 % of landslide and non-landslide points were randomly selected for logistic regression, and the others were used for model validation. For evaluating the accuracy of predictive models, this paper adopts several criteria including receiver operating characteristic (ROC) curve method. Logistic regression experiments found all five factors to be significant in explaining landslide occurrence on global scale. During the modeling process, percentage correct in confusion matrix of landslide classification was approximately 80 % and the area under the curve (AUC) was nearly 0.87. During the validation process, the above statistics were about 81 % and 0.88, respectively. Such result indicates that the model has strong robustness and stable performance. This model found that at a global scale, soil moisture can be dominant in the occurrence of landslides and topographic factor may be secondary.

Temporal Variations in Landslide Distributions Following Extreme Events: Implications for Landslide Susceptibility Modeling

“…Such multi-temporal seasonal and historic inventories are typically developed using satellite-based monitoring of a given region (e.g., Behling et al, 2016). Landslide susceptibility models are commonly applied across a range of spatial scales (Cascini, 2008), from individual slope units (e.g., Alvioli et al, 2016;Amato et al, 2019) to catchments (e.g., Conforti et al, 2012;Romer & Ferentinou, 2016) to geographical regions (e.g., Sabatakakis et al, 2013;Thi Ngo et al, 2020) and even globally (e.g., L. Lin et al, 2017;Stanley & Kirschbaum, 2017). Statistical landslide susceptibility modeling is very common (Reichenbach et al, 2018), and is often a fundamental component of landslide hazard analyses, risk assessments, land-use planning, and early warning systems (e.g., Fell et al, 2008;Palau et al, 2020;van Westen et al, 2008).…”

High-Resolution Landslide Susceptibility Map Generation using Machine Learning (Case Study in Pacitan, Indonesia)

“…The statistical approaches have become popular in the use of remote sensing (RS) with a geographic information system (GIS) [7]. There are many statistical approaches used in landslide susceptibility assessment, including a frequency ratio (FR) [8], [9], statistical index (SI) [10], as well as logistic regression (LR) [11], [12]. Furthermore, the approaches using machine learning techniques have become popular recently.…”

Spatial landslide susceptibility mapping using integrating an adaptive neuro-fuzzy inference system (ANFIS) with two multi-criteria decision-making approaches

“…This mass movement occurs whenever the loading of an earth material exceeds its shear strength (Lin et al 2017). Although this geological phenomenon is often triggered by earthquakes and heavy rainfalls, the expansion of anthropogenic activities in susceptible areas has always played an important factor in its occurrence (Baena et al 2019) Despite the increased human knowledge regarding landslide occurrence and factors controlling this phenomenon, it is believed that the damage caused by landslides will increase due to deforestation, climate change and urban development (Pham and Prakash 2018).…”