A Cloud Model based Landslide Risk Level Assesment method

Authors: Jing Geng*, School of Computer Science & Technology, Beijing Institute of Technology, Beijing, China, Wenxia Gan, School of Civil Engineering and Architecture, Wuhan Institute of Technology, Wuhan, China, Hanning Yuan, School of Computer Science & Technology, Beijing Institute of Technology´╝îBeijing , China, Jiakai Yang, School of Computer Science & Technology, Beijing Institute of Technology, Beijing, China
Topics: Hazards, Risks, and Disasters, Geographic Information Science and Systems
Keywords: landslide risk level assessment,cloud model, comprehensive
Session Type: Paper
Day: 4/6/2019
Start / End Time: 9:55 AM / 11:35 AM
Room: Balcony B, Marriott, Mezzanine Level
Presentation File: No File Uploaded

Landslide always occurs suddenly but be harmful greatly. When emergencies happen, making proper and efficient treatment scheme is vital, while valuable information is the key to make such decisions. However, information about disaster provided by the existing method is usually not only inconveniently accessible but also difficult to be interpreted by non-professionals, thus hard to provide quick and efficient help for official decision makers from the competent departments who is usually lack of necessary Geoscience expertise.
Aims at this problem, our study is to provide easy-comprehensible information corresponding to the landslide risk level. The essence of the study is the cloud model based risk level assessment method, which interprets and integrates the various attributes and finally generates one easily comprehensible evaluation index, based on the robust applicability of cloud models in the handling of qualitative concepts. Specific work includes the following:
1) Obtain the spatial information of landslide by comparison between the pre-disaster and post-disaster DEMs (Digital Elevation Model), combining with auxiliary data (multi-spectrum optical imageries, LULC data).
2) Construct the grading rules of risk assessment factors based on summarizing and analyzing the landslides records in the recent decade.
3) Adapt parameters Ex, En and He of the cloud model and generate the forward cloud generator for each evaluation indicator. By weighted integration of the certainty degree corresponding to each index, the comprehensive determination degree of the landslide disaster will be given, which reflects the risk level of the disaster.

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