Journal
ENVIRONMENTAL EARTH SCIENCES
Volume 76, Issue 19, Pages -Publisher
SPRINGER
DOI: 10.1007/s12665-017-6981-2
Keywords
Landslides; GIS; Support vector machines; Random subspace
Funding
- National Natural Science Foundation of China [41431177, 41601413]
- Natural Science Research Programme of Jiangsu [BK20150975, 14KJA170001]
- National Basic Research Programme of China [2015CB954102]
- Vilas Associate Award
- Hammel Faculty Fellow Award
- Manasse Chair Professorship from the University of Wisconsin-Madison
- One-Thousand Talents Programme of China
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This study proposed a hybrid modeling approach using two methods, support vector machines and random subspace, to create a novel model named random subspacebased support vector machines (RSSVM) for assessing landslide susceptibility. The newly developed model was then tested in the Wuning area, China, to produce a landslide susceptibility map. With the purpose of achieving the objective of the study, a spatial dataset was initially constructed that includes a landslide inventory map consisting of 445 landslide regions. Then, various landslide-influencing factors were defined, including slope angle, aspect, altitude, topographic wetness index, stream power index, sediment transport index, soil, lithology, normalized difference vegetation index, land use, rainfall, distance to roads, distance to rivers, and distance to faults. Next, the result of the RSSVM model was validated using statistical index-based evaluations and the receiver operating characteristic curve approach. Then, to evaluate the performance of the suggested RSSVM model, a comparison analysis was performed to other existing approaches such as artificial neural network, Naive Bayes (NB) and support vector machine (SVM). In general, the performance of the RSSVM model was better than the other models for spatial prediction of landslide susceptibility. The AUC results of the applied models are as follows: RSSVM (AUC = 0.857), followed by MLP (AUC = 0.823), SVM (AUC = 0.814) and NB (AUC = 0.783). The present study indicates that RSSVM can be used for landslide susceptibility evaluation, and the results are very useful for local governments and people living in the Wuning area.
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