Abstract
Landslide hazard assessment at the Mu Cang Chai district; Yen Bai province (Viet Nam) has been done using Random SubSpace fuzzy rules based Classifier Ensemble (RSSCE) method and probability analysis of rainfall data. RSSCE which is a novel classifier ensemble method has been applied to predict spatially landslide occurrences in the area. Prediction of temporally landslide occurrences in the present study has been done using rainfall data for the period 2008–2013. A total of fifteen landslide influencing factors namely slope, aspect, curvature, plan curvature, profile curvature, elevation, land use, lithology, rainfall, distance to faults, fault density, distance to roads, road density, distance to rivers, and river density have been utilized. The result of the analysis shows that RSSCE and probability analysis of rainfall data are promising methods for landslide hazard assessment. Finally, landslide hazard map has been generated by integrating spatial prediction and temporal probability analysis of landslides for the land use planning and landslide hazard management.
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Acknowledgments
Authors would like to sincerely thank to the Vietnam Institute of Geosciences and Mineral Resources for sharing the data for the present study. Authors are also thankful to the Director, Bhaskarcharya Institute for Space Applications and Geo-Informatics (BISAG), Department of Science and Technology, Government of Gujarat, Gandhinagar, Gujarat, India for the encouragement and for providing facilities to carry out this research work.
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Pham, B.T., Tien Bui, D., Pham, H.V. et al. Landslide Hazard Assessment Using Random SubSpace Fuzzy Rules Based Classifier Ensemble and Probability Analysis of Rainfall Data: A Case Study at Mu Cang Chai District, Yen Bai Province (Viet Nam). J Indian Soc Remote Sens 45, 673–683 (2017). https://doi.org/10.1007/s12524-016-0620-3
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DOI: https://doi.org/10.1007/s12524-016-0620-3