Location-allocation modeling for emergency evacuation planning with GIS and remote sensing: A case study of Northeast Bangladesh

被引:58
作者
Rahman, Mahfuzur [1 ,2 ,3 ]
Chen, Ningsheng [1 ,2 ]
Islam, Md Monirul [3 ]
Dewan, Ashraf [4 ]
Pourghasemi, Hamid Reza [5 ]
Washakh, Rana Muhammad Ali [6 ,7 ]
Nepal, Nirdesh [8 ]
Tian, Shufeng [1 ,2 ]
Faiz, Hamid [1 ,2 ]
Alam, Mehtab [1 ,2 ]
Ahmed, Naveed [2 ]
机构
[1] Chinese Acad Sci, Inst Mt Hazards & Environm IMHE, Key Lab Mt Hazards & Earth Surface Proc, Chengdu 610041, Peoples R China
[2] Univ Chinese Acad Sci UCAS, Beijing 100049, Peoples R China
[3] Int Univ Business Agr & Technol IUBAT, Dept Civil Engn, Dhaka 1230, Bangladesh
[4] Curtin Univ, Sch Earth & Planetary Sci, Kent St, Bentley, WA 6102, Australia
[5] Shiraz Univ, Coll Agr, Dept Nat Resources & Environm Engn, Shiraz, Iran
[6] Neijiang Normal Univ, Sch Architecture, Neijiang 641100, Peoples R China
[7] Chinese Acad Sci, Inst Tibetan Plateau Res, Beijing 100101, Peoples R China
[8] Global Inst Interdisciplinary Studies, Kathmandu 3084, Nepal
基金
中国国家自然科学基金;
关键词
Natural disasters; Emergency evacuation centers; Flooding; Machine learning; Multi-criteria decision making; Location-allocation model; MULTICRITERIA DECISION-MAKING; SUPPORT VECTOR MACHINE; FLOOD HAZARD; CLIMATE-CHANGE; SUSCEPTIBILITY; AREA; VULNERABILITY; WEIGHTS;
D O I
10.1016/j.gsf.2020.09.022
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
This work developed models to identify optimal spatial distribution of emergency evacuation centers (EECs) such as schools, colleges, hospitals, and fire stations to improve flood emergency planning in the Sylhet region of northeastern Bangladesh. The use of location-allocation models (LAMs) for evacuation in regard to flood victims is essential to minimize disaster risk. In the first step, flood susceptibility maps were developed using machine learning models (MLMs), including: Levenberg-Marquardt back propagation (LM-BP) neural network and decision trees (DT) and multi-criteria decision making (MCDM) method. Performance of the MLMs and MCDM techniques were assessed considering the area under the receiver operating characteristic (AUROC) curve. Mathematical approaches in a geographic information system(GIS) for four well-known LAM problems affecting emergency rescue time are proposed: maximal covering location problem (MCLP), the maximize attendance (MA), p-median problem (PMP), and the location set covering problem(LSCP). The results showed that existing EECs were not optimally distributed, and that some areas were not adequately served by EECs (i.e., not all demand points could be reached within a 60-min travel time). We concluded that the proposed models can be used to improve planning of the distribution of EECs, and that application of the models could contribute to reducing human casualties, property losses, and improve emergency operation. (C) 2021 ChinaUniversity of Geosciences (Beijing) and Peking University. Production and hosting by Elsevier B.V.
引用
收藏
页数:17
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