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Open AccessArticle

A Novel Rule-Based Approach in Mapping Landslide Susceptibility

1
Discipline of Geography and Spatial Sciences, School of Technology, Environments and Design, University of Tasmania, Churchill Ave, Hobart, TAS 7005, Australia
2
Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), University of Technology Sydney, NSW 2007, Australia
3
Department of Energy and Mineral Resources Engineering, Choongmu-gwan, Sejong University, 209 Neungdongro Gwangjin-gu, Seoul 05006, Korea
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(10), 2274; https://doi.org/10.3390/s19102274
Received: 15 April 2019 / Revised: 13 May 2019 / Accepted: 14 May 2019 / Published: 16 May 2019
Despite recent advances in developing landslide susceptibility mapping (LSM) techniques, resultant maps are often not transparent, and susceptibility rules are barely made explicit. This weakens the proper understanding of conditioning criteria involved in shaping landslide events at the local scale. Further, a high level of subjectivity in re-classifying susceptibility scores into various classes often downgrades the quality of those maps. Here, we apply a novel rule-based system as an alternative approach for LSM. Therein, the initially assembled rules relate landslide-conditioning factors within individual rule-sets. This is implemented without the complication of applying logical or relational operators. To achieve this, first, Shannon entropy was employed to assess the priority order of landslide-conditioning factors and the uncertainty of each rule within the corresponding rule-sets. Next, the rule-level uncertainties were mapped and used to asses the reliability of the susceptibility map at the local scale (i.e., at pixel-level). A set of If-Then rules were applied to convert susceptibility values to susceptibility classes, where less level of subjectivity is guaranteed. In a case study of Northwest Tasmania in Australia, the performance of the proposed method was assessed by receiver operating characteristics’ area under the curve (AUC). Our method demonstrated promising performance with AUC of 0.934. This was a result of a transparent rule-based approach, where priorities and state/value of landslide-conditioning factors for each pixel were identified. In addition, the uncertainty of susceptibility rules can be readily accessed, interpreted, and replicated. The achieved results demonstrate that the proposed rule-based method is beneficial to derive insights into LSM processes. View Full-Text
Keywords: Shannon entropy; uncertainty; landslide susceptibility mapping (LSM); GIS; Tasmania Shannon entropy; uncertainty; landslide susceptibility mapping (LSM); GIS; Tasmania
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Roodposhti, M.S.; Aryal, J.; Pradhan, B. A Novel Rule-Based Approach in Mapping Landslide Susceptibility. Sensors 2019, 19, 2274.

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