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

Spatial prediction of rainfall-induced shallow landslides using gene expression programming... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Natural Hazards Springer Journals

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

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Publisher
Springer Netherlands
Copyright
Copyright © 2018 by Springer Science+Business Media B.V., part of Springer Nature
Subject
Earth Sciences; Natural Hazards; Hydrogeology; Geophysics/Geodesy; Geotechnical Engineering & Applied Earth Sciences; Civil Engineering; Environmental Management
ISSN
0921-030X
eISSN
1573-0840
D.O.I.
10.1007/s11069-018-3286-z
Publisher site
See Article on Publisher Site

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.

Journal

Natural HazardsSpringer Journals

Published: Mar 30, 2018

References

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