Spatial prediction of rainfall-induced shallow landslides using gene expression programming integrated with GIS: a case study in Vietnam

Nhat-Duc Hoang () and Dieu Tien Bui ()
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Nhat-Duc Hoang: Duy Tan University
Dieu Tien Bui: University College of Southeast Norway

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2018, vol. 92, issue 3, No 30, 1887 pages

Abstract: Abstract Shallow landslide represents one of the most devastating morphodynamic processes that bring about great destructions to human life and infrastructure. Landslide spatial prediction can significantly help government agencies in land use and mitigation measure planning. Nevertheless, landslide spatial modeling remains a very challenging problem due to its inherent complexity. This study proposes an integration of geographical information system (GIS) and gene expression programming (GEP) for predicting rainfall-induced shallow landslide occurrences in Son La Province, Vietnam. A landslide inventory map has been constructed based on historical landslide locations. Furthermore, a dataset which features 12 influencing factors is collected using GIS technology. Based on the GEP algorithm and the collected dataset, an empirical model for spatial prediction of the shallow landslide has been established by means of natural selection. The predictive capability of the model has been verified by the area under the curve calculation. Experimental results point out that the newly proposed approach is a promising tool for shallow landslide prediction.

Keywords: Shallow landslide; Rainfall-induced; Gene expression programming; Geographical information system; Artificial intelligence (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s11069-018-3286-z

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