Mapping landslide release area using Random Forest Model

M. R. Darminto, Hone Jay Chu

Research output: Contribution to journalConference articlepeer-review

4 Citations (Scopus)


Landslides pose threats not only to infrastructure around the world but also to local communities. One particularly susceptible area in Taiwan is in the Zhoukou River basin, Kaoping watershed. Landslides source area plays an important role in landslide occurrence, where the triggering stage initiates the failure. The conditions of landslide source area are assumed to be the same in the future. This study aimed to produce a Random Forest model that accurately predicts future landslide release in this area by validating the predictions against those observed landslide releases in this region. The landslide data were recorded in the year of 2010, a year after typhoon Morakot stroked Taiwan in 2009, triggering huge number of landslides all over the country. This study proposed the new concept to separate landslides area into release as its original source and focuses on using the topographical factors derived from Digital Elevation Model (DEM) as the independent variable in predicting landslides occurrence, including Slope, Aspect, Curvature, Topographic Wetness Index, Average Slope and Distance from the river, and an additional geological map of the study area. An observed landslide release occurrence layer posed as the dependent variable classifier in the model. First, data sampling strategies applied show an optimal model to be created with the highest Area Under Curve (AUC) value of 0.814. Next, this model identified the most influential factors causing landslides. Aspect, were determined as being most influential factor, where Distance from river, and Slope as second and third most influential. The concept of release area separation showed a better AUC value model compared to the model using conventional full landslide inventory. The random forest model also showed a reliable result when compared to logistic regression and decision tree using the same data sampling, with the AUC value of 0.814, 0.65, and 0.728 respectively. The results have proven that random forest model is suitable for producing landslide release susceptibility map.

Original languageEnglish
Article number012038
JournalIOP Conference Series: Earth and Environmental Science
Issue number1
Publication statusPublished - 13 Dec 2019
Externally publishedYes
Event4th Geomatics International Conference 2019, GeoICON 2019 - Surabaya, Indonesia
Duration: 21 Aug 201922 Aug 2019


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