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Weights of evidence method for landslide susceptibility mapping. Prahova Subcarpathians, Romania

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  • Iuliana Armaş

Abstract

The goal of this paper is to assess the landslide susceptibility of a hilly area in the Subcarpathian sector of the Prahova Valley, using the weight of evidence statistical method. This method aims to reduce the multitude of landslide-related conditions to a pattern of a few discrete predictive variables. The method is based on the decision of which state is more likely to occur grounded on the presence or absence of a predictive variable and the occurrence of an event (e.g., landslide) within a pixel. Based on the chi-square test and the Pearson correlation applied on the data, the selected conditionally independent variables in this study were as follows: slope gradient, slope aspect, and land use. Weights calculated individually for the three themes were added to produce a probability estimate of the area. The predictive power of the map was tested on the basis of a split sample of landslides that were not used in the modeling process. The fact that a great percent of the declivitous surfaces are susceptible to landslides shows the dominant manner of the evolution of the Subcarpathian slopes, the acceleration or deceleration of the process being influenced by the land use. Copyright Springer Science+Business Media B.V. 2012

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  • Iuliana Armaş, 2012. "Weights of evidence method for landslide susceptibility mapping. Prahova Subcarpathians, Romania," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 60(3), pages 937-950, February.
  • Handle: RePEc:spr:nathaz:v:60:y:2012:i:3:p:937-950
    DOI: 10.1007/s11069-011-9879-4
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    1. Maria Kouli & Constantinos Loupasakis & Pantelis Soupios & Filippos Vallianatos, 2010. "Landslide hazard zonation in high risk areas of Rethymno Prefecture, Crete Island, Greece," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 52(3), pages 599-621, March.
    2. C. van Westen & N. Rengers & R. Soeters, 2003. "Use of Geomorphological Information in Indirect Landslide Susceptibility Assessment," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 30(3), pages 399-419, November.
    3. Chang-Jo Chung & Andrea Fabbri, 2003. "Validation of Spatial Prediction Models for Landslide Hazard Mapping," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 30(3), pages 451-472, November.
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    2. Quynh Duy Bui & Hang Ha & Dong Thanh Khuc & Dinh Quoc Nguyen & Jason von Meding & Lam Phuong Nguyen & Chinh Luu, 2023. "Landslide susceptibility prediction mapping with advanced ensemble models: Son La province, Vietnam," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(2), pages 2283-2309, March.
    3. Romulus Costache & Alireza Arabameri & Iulia Costache & Anca Crăciun & Binh Thai Pham, 2022. "New Machine Learning Ensemble for Flood Susceptibility Estimation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(12), pages 4765-4783, September.
    4. Tingyu Zhang & Quan Fu & Hao Wang & Fangfang Liu & Huanyuan Wang & Ling Han, 2022. "Bagging-based machine learning algorithms for landslide susceptibility modeling," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 110(2), pages 823-846, January.
    5. Nitin L. Rane & Geetha K. Jayaraj, 2022. "Comparison of multi-influence factor, weight of evidence and frequency ratio techniques to evaluate groundwater potential zones of basaltic aquifer systems," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(2), pages 2315-2344, February.

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