Journal
SUSTAINABILITY
Volume 10, Issue 10, Pages -Publisher
MDPI
DOI: 10.3390/su10103697
Keywords
machine-learning algorithm; Logistic regression; LogitBoost; NaiveBayes; receiver operating characteristics
Funding
- National Research Foundation of Korea (NRF) - Korea government (MSIP) [2016K1A3A1A09915721, 2017R1A2B4003258]
- Ministry of Science and ICT
- National Research Council of Science & Technology (NST), Republic of Korea [18-3111-1] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
- National Research Foundation of Korea [2017R1A2B4003258, 2016K1A3A1A09915721] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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The occurrence of landslide in the hilly region of South Korea is a matter of serious concern. This study tries to produce landslide susceptibility maps for Jumunjin Country in South Korea. Three machine learning algorithms, namely Logistic Regression (LR), LogitBoost (LB), and NaiveBayes (NB) are used, and their final model outcomes are compared to each other. Firstly, a landslide inventory map and the associated input data layers of the landslide conditioning factors were developed based on field verification, historical records, and high-resolution remote-sensing data in the geographic information system (GIS) environment. Seventeen landslide conditioning factors were prepared, including aspect, slope, altitude, maximum curvature, profile curvature, topographic wetness index (TWI), topographic positioning index (TPI), distance from fault, convexity, forest type, forest diameter, forest density, land use/land cover, lithology, soil, flow accumulation, and mid slope position. The result showed that the area under the curve (AUC) values of LR, LB, and NB models were 84.2%, 70.7%, and 85.2%, respectively. The results revealed that the LR and LB models produced reasonable accuracy than respect to NB model in landslide susceptibility assessment. The final susceptibility maps would be useful for preliminary land-use planning and hazard mitigation purpose.
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