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Remote Sens. 2017, 9(4), 304; doi:10.3390/rs9040304

Application of Bivariate and Multivariate Statistical Techniques in Landslide Susceptibility Modeling in Chittagong City Corporation, Bangladesh

1
UCL Institute for Risk and Disaster Reduction, Department of Earth Sciences, University College London (UCL), Gower Street, London WC1E 6BT, UK
2
Department of Disaster Science and Management, Faculty of Earth and Environmental Sciences, University of Dhaka, Dhaka 1000, Bangladesh
3
Department of Spatial Sciences, Faculty of Science and Engineering, Curtin University, Perth 6102, Australia
*
Author to whom correspondence should be addressed.
Academic Editors: Zhong Lu, Chaoying Zhao and Prasad S. Thenkabail
Received: 12 January 2017 / Revised: 9 March 2017 / Accepted: 15 March 2017 / Published: 23 March 2017
(This article belongs to the Special Issue Remote Sensing of Landslides)

Abstract

The communities living on the dangerous hillslopes in Chittagong City Corporation (CCC) in Bangladesh recurrently experience landslide hazards during the monsoon season. The frequency and intensity of landslides are increasing over time because of heavy rainfall occurring over a few days. Furthermore, rapid urbanization through hill-cutting is another factor, which is believed to have a significant impact on the occurrence of landslides. This study aims to develop landslide susceptibility maps (LSMs) through the use of Dempster-Shafer weights of evidence (WoE) and the multiple regression (MR) method. Three different combinations with principal component analysis (PCA) and fuzzy membership techniques were used and tested. Twelve factor maps (i.e., slope, hill-cutting, geology, geomorphology, NDVI, soil moisture, precipitation and distance from existing buildings, stream, road and drainage network, and faults-lineaments) were prepared based on their association with historical landslide events. A landslide inventory map was prepared through field surveys for model simulation and validation purposes. The performance of the predicted LSMs was validated using the area under the relative operating characteristic (ROC) curve method. The overall success rates were 87.3%, 90.9%, 91.3%, and 93.9%, respectively for the WoE, MR with all the layers, MR with PCA layers, and MR with fuzzy probability layers. View Full-Text
Keywords: landslides; regression statistics; weights of evidence; cartography; remote sensing; GIS landslides; regression statistics; weights of evidence; cartography; remote sensing; GIS
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Ahmed, B.; Dewan, A. Application of Bivariate and Multivariate Statistical Techniques in Landslide Susceptibility Modeling in Chittagong City Corporation, Bangladesh. Remote Sens. 2017, 9, 304.

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