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http://dx.doi.org/10.7780/kjrs.2019.35.2.9

Landslide Susceptibility Prediction using Evidential Belief Function, Weight of Evidence and Artificial Neural Network Models  

Lee, Saro (Geoscience Platform Research Division, Korea Institute of Geoscience and Mineral Resources)
Oh, Hyun-Joo (Geologic Environment Division, Korea Institute of Geoscience and Mineral Resources)
Publication Information
Korean Journal of Remote Sensing / v.35, no.2, 2019 , pp. 299-316 More about this Journal
Abstract
The purpose of this study was to analyze landslide susceptibility in the Pyeongchang area using Weight of Evidence (WOE) and Evidential Belief Function (EBF) as probability models and Artificial Neural Networks (ANN) as a machine learning model in a geographic information system (GIS). This study examined the widespread shallow landslides triggered by heavy rainfall during Typhoon Ewiniar in 2006, which caused serious property damage and significant loss of life. For the landslide susceptibility mapping, 3,955 landslide occurrences were detected using aerial photographs, and environmental spatial data such as terrain, geology, soil, forest, and land use were collected and constructed in a spatial database. Seventeen factors that could affect landsliding were extracted from the spatial database. All landslides were randomly separated into two datasets, a training set (50%) and validation set (50%), to establish and validate the EBF, WOE, and ANN models. According to the validation results of the area under the curve (AUC) method, the accuracy was 74.73%, 75.03%, and 70.87% for WOE, EBF, and ANN, respectively. The EBF model had the highest accuracy. However, all models had predictive accuracy exceeding 70%, the level that is effective for landslide susceptibility mapping. These models can be applied to predict landslide susceptibility in an area where landslides have not occurred previously based on the relationships between landslide and environmental factors. This susceptibility map can help reduce landslide risk, provide guidance for policy and land use development, and save time and expense for landslide hazard prevention. In the future, more generalized models should be developed by applying landslide susceptibility mapping in various areas.
Keywords
Landslide; Evidential Belief Function; Weight of Evidence; Artificial Neural Network; GIS;
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