Abstract
Landslides are typically triggered by earthquakes or rainfall occasionally a rainfall event followed by an earthquake or vice versa. Yet, most of the works presented in the past decade have been largely focused at the single event-susceptibility model. Such type of modeling is found insufficient in places where the triggering mechanism involves both factors such as one found in the Chuetsu region, Japan. Generally, a single event model provides only limited enlightenment of landslide spatial distribution and thus understate the potential combination-effect interrelation of earthquakes- and rainfall-triggered landslides. This study explores the both-effect of landslides triggered by Chuetsu-Niigata earthquake followed by a heavy rainfall event through examining multiple traditional statistical models and data mining for understanding the coupling effects. This paper aims to compare the abilities of the statistical probabilistic likelihood-frequency ratio (PLFR) model, information value (InV) method, certainty factors (CF), artificial neural network (ANN) and ensemble support vector machine (SVM) for the landslide susceptibility mapping (LSM) using high-resolution-light detection and ranging digital elevation model (LiDAR DEM). Firstly, the landslide inventory map including 8459 landslide polygons was compiled from multiple aerial photographs and satellite imageries. These datasets were then randomly split into two parts: 70% landslide polygons (5921) for training model and the remaining polygons for validation (2538). Next, seven causative factors were classified into three categories namely topographic factors, hydrological factors and geological factors. We then identified the associations between landslide occurrence and causative factors to produce LSM. Finally, the accuracies of five models were validated by the area under curves (AUC) method. The AUC values of five models vary from 0.77 to 0.87. Regarding the capability of performance, the proposed SVM is promising for constructing the regional landslide-prone potential areas using both types of landslides. Additionally, the result of our LSM can be applied for similar areas which have been experiencing both rainfall-earthquake landslides.
Highlights
Among the various natural hazards, landslides are recognized as one of the most destructive and hazardous threats in several parts of the mountainous world
We address two research questions in this paper: (i) do the sophisticated data mining methods provide a better predictive competency compared with the traditional statistical methods? And (ii) how different the results while using multi-type landslides instead of single type landslides? For achieving the first objective, we analyze and compare the accuracy of landslide susceptibility mapping (LSM) maps generated by five different techniques including three traditional statistical methods, that is, probabilistic likelihood-frequency ratio model (PLFR), information value (InV), certainty factor approach (CF); and the two machine learning techniques namely, artificial neural network (ANN) and support vector machine (SVM) in a regional-scale analysis
The results show that PLFR values increase with increasing altitudes till it reaches 357 m elevation in the study area
Summary
Among the various natural hazards, landslides are recognized as one of the most destructive and hazardous threats in several parts of the mountainous world. It has been noticed that about 5% of all fatalities in earthquake events are caused by coseismic landslides, in some cases even more [1]. The recent Hokkaido Eastern Iburi earthquake on 6 September 2018, about 80% of the fatalities are caused by the landslides alone [2]. The increased amount of urbanization and economic development together with the unusual frequency of severe regional precipitations owing to global climate change, the landslide hazard losses are expected to rise in the future [7,8,9]. To mitigate and reduce the economic losses and risks associated with the landslide hazards, there is an urgent requirement to identify and map the landslide-prone areas
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