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Environmental Earth Sciences

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01.08.2019 | Original Article

Landslide susceptibility evaluating using artificial intelligence method in the Youfang district (China)

verfasst von: Haoyuan Hong, Junzhi Liu, A-Xing Zhu

Erschienen in: Environmental Earth Sciences | Ausgabe 15/2019

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Abstract

This study assesses the landslide susceptibility of the Youfang area, China. For this purpose, four advanced artificial intelligence models, namely, Naïve Bayes (NB), multilayer perceptron (MLP), kernel logistic regression (KLR), and J48-bagging methods, were applied and compared. The relationship between landslides happening and landslide conditioning factors which include: slope, aspect, altitude, plan curvature, profile curvature, stream power index (SPI), topographic wetness index (TWI), sediment transport index (STI), landuse, lithology, distance to faults, distance to roads, distance to rivers, and rainfall were analyzed by the frequency ratios method. These results indicated that MLP model exhibits the most stable and reasonable result, and the resultant landslide susceptibility maps are a useful tool for local government managers and policy planners for this study area and other areas.

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Titel: Landslide susceptibility evaluating using artificial intelligence method in the Youfang district (China)
verfasst von: Haoyuan Hong
Junzhi Liu
A-Xing Zhu
Publikationsdatum: 01.08.2019
Verlag: Springer Berlin Heidelberg
Erschienen in: Environmental Earth Sciences / Ausgabe 15/2019
Print ISSN: 1866-6280
Elektronische ISSN: 1866-6299
DOI: https://doi.org/10.1007/s12665-019-8415-9

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