Machine learning and artificial intelligence models development in rainfall-induced landslide prediction

• Published in: IAES International Journal of Artificial Intelligence Vol. 12; no. 1; p. 262
• Main Authors: Harsa, Hastuadi, Hidayat, Anistia Malinda, Mulsandi, Adi, Suprihadi, Bambang, Kurniawan, Roni, Habibie, Muhammad Najib, Hutapea, Thahir Daniel, Swarinoto, Yunus S., Syahputra Makmur, Erwin Eka, Fitria, Welly, Sri Sudewi, Rahayu Sapta, Praja, Alfan Sukmana
• Format: Journal Article
• Language: English
• Published: Yogyakarta IAES Institute of Advanced Engineering and Science 01.03.2023
• Subjects: Algorithms; Artificial intelligence; Deep learning; Disaster management; Generalized linear models; Landslides; Machine learning; Precipitation; Prediction models; Rainfall; Satellite observation; Statistical models
• ISSN: 2089-4872; 2252-8938; 2089-4872
• DOI: 10.11591/ijai.v12.i1.pp262-270

In Indonesia, rainfall is one crucial triggering factor for landslides. This paper aims to build landslide event prediction models using several machine learning and artificial intelligence algorithms. The algorithms were trained with two different methods. The input of the algorithms was precipitation data obtained from the global satellite mapping of precipitation satellite observation, and the target was landslide event occurrence data obtained from the Indonesian National Board for Disaster Management. Each algorithm provided some model candidates with different parameter settings for each method. As a result, there were 52 and 72 model candidates for both methods. The best model was then chosen from each method. The result shows that the model generated by generalized linear model was the best model for the first method and deep learning for the second one. Furthermore, the best models at each method gained 0.828 and 0.836 for the area under receiver operating characteristics curve, and their log-loss were 0.156 and 0.154. The second method, which used input data transformation, provided better performance.