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

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Geoscience Frontiers 2021年第3期12卷 169-185页

作者： Mahfuzur Rahman Ningsheng Chen mdmonirul islam Ashraf Dewan Hamid Reza Pourghasemi Rana Muhammad AliWashakh Nirdesh Nepal Shufeng Tian Hamid Faiz Mehtab Alam Naveed Ahmed Key Laboratory for Mountain Hazards and Earth Surface Process Institute of Mountain Hazards and Environment(IMHE)Chinese Academy of Sciences(CAS)Chengdu 610041China University of Chinese Academy of Sciences(UCAS) Beijing 100049China Department of Civil Engineering International University of Business Agriculture and Technology(IUBAT)Dhaka 1230Bangladesh School of Earth and Planetary Sciences Curtin UniversityKent StBentleyWA 6102Australia Department of Natural Resources and Environmental Engineering College of AgricultureShiraz UniversityShirazIran School of Architecture Neijiang Normal UniversityNeijiang 641100China Institute of Tibetan Plateau Research Chinese Academy of SciencesBeijing 100101China Global Institute for Interdisciplinary Studies Kathmandu 3084Nepal

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 *** use of location-allocation models(LAMs)for evacuation in regard to flood victims is essential to minimize disaster *** 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)*** of the MLMs and MCDM techniques were assessed considering the area under the receiver operating characteristic(AUROC)*** 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.

关键词： Natural disasters Emergency evacuation centers Flooding Machine learning Multi-criteria decision making Location-allocation model

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