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- W2897668753 abstract "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 NaïveBayes (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." @default.
- W2897668753 created "2018-10-26" @default.
- W2897668753 creator A5006705342 @default.
- W2897668753 creator A5045171576 @default.
- W2897668753 creator A5050392348 @default.
- W2897668753 creator A5057108257 @default.
- W2897668753 creator A5077439959 @default.
- W2897668753 date "2018-10-15" @default.
- W2897668753 modified "2023-10-16" @default.
- W2897668753 title "Assessment of Landslide-Prone Areas and Their Zonation Using Logistic Regression, LogitBoost, and NaïveBayes Machine-Learning Algorithms" @default.
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- W2897668753 doi "https://doi.org/10.3390/su10103697" @default.
- W2897668753 hasPublicationYear "2018" @default.
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