| Title |
Landslide Susceptibility Mapping by Comparing GIS-based Spatial Models in the Java, Indonesia |
| Authors |
김미경(Kim, Mi-Kyeong) ; 김상필(Kim, Sangpil) ; 노현주(Nho, Hyunju) ; 손홍규(Sohn, Hong-Gyoo) |
| DOI |
https://doi.org/10.12652/Ksce.2017.37.5.0927 |
| Keywords |
산사태;취약성;자바;의사결정트리;인공신경망 Landslide;Susceptibility;Java;WoE;Decision tree;Artificial neural network |
| Abstract |
Landslide has been a major disaster in Indonesia, and recent climate change and indiscriminate urban development around the mountains have increased landslide risks. Java Island, Indonesia, where more than half of Indonesia's population lives, is experiencing a great deal of damage due to frequent landslides. However, even in such a dangerous situation, the number of inhabitants residing in the landslide-prone area increases year by year, and it is necessary to develop a technique for analyzing landslide-hazardous and vulnerable areas. In this regard, this study aims to evaluate landslide susceptibility of Java, an island of Indonesia, by using GIS-based spatial prediction models. We constructed the geospatial database such as landslide locations, topography, hydrology, soil type, and land cover over the study area and created spatial prediction models by applying Weight of Evidence (WoE), decision trees algorithm and artificial neural network. The three models showed prediction accuracy of 66.95%, 67.04%, and 69.67%, respectively. The results of the study are expected to be useful for prevention of landslide damage for the future and landslide disaster management policies in Indonesia. |