Journal
ENVIRONMENTAL EARTH SCIENCES
Volume 78, Issue 15, Pages -Publisher
SPRINGER
DOI: 10.1007/s12665-019-8415-9
Keywords
Landslide; Naive Bayes; Kernel logistic regression; Multilayer Perceptron; J48-Bagging
Funding
- National Natural Science Foundation of China [41431177, 41871300]
- National Basic Research Program of China [2015CB954102]
- Postgraduate Research & Practice Innovation Program of Jiangsu Province [KYCX19_0785]
- Outstanding Innovation Team in Colleges and Universities in Jiangsu Province
- Vilas Associate Award from the University of Wisconsin-Madison
- Hammel Faculty Fellow Award from the University of Wisconsin-Madison
- Manasse Chair Professorship from the University of Wisconsin-Madison
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This study assesses the landslide susceptibility of the Youfang area, China. For this purpose, four advanced artificial intelligence models, namely, Naive 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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