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

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 Mahfuzur Rahman;Ningsheng Chen;Md Monirul Islam;Ashraf Dewan;Hamid Reza Pourghasemi;Rana Muhammad Ali Washakh;Nirdesh Nepal;Shufeng Tian;Hamid Faiz;Mehtab Alam;Naveed Ahmed

作者机构：Key Laboratory for Mountain Hazards and Earth Surface ProcessInstitute 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 EngineeringInternational University of Business Agriculture and Technology(IUBAT)Dhaka 1230Bangladesh School of Earth and Planetary SciencesCurtin UniversityKent StBentleyWA 6102Australia Department of Natural Resources and Environmental EngineeringCollege of AgricultureShiraz UniversityShirazIran School of ArchitectureNeijiang Normal UniversityNeijiang 641100China Institute of Tibetan Plateau ResearchChinese Academy of SciencesBeijing 100101China Global Institute for Interdisciplinary StudiesKathmandu 3084Nepal 

出 版 物：《Geoscience Frontiers》 (地学前缘（英文版）)

年 卷 期：2021年第12卷第3期

页      面：169-185页

核心收录：

学科分类：081504[工学-水利水电工程] 08[工学] 0815[工学-水利工程] 

基　　金：funded by the National Natural Science Foundation of China(Grant Nos.41861134008 and 41671112) the 135 Strategic Program of the Institute of Mountain Hazards and Environment(IMHE),Chinese Academy of Sciences(CAS)(Grant No.SDS-135-1705) 

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

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 *** 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.