Location of Disaster Management Bases Using Spatial Analysis
Pre-crisis management involves the optimal selection of relief and rescue centers to minimize vulnerability. Iran is particularly vulnerable due to its location on the Alpine-Himalaya seismic belt, resulting in an average death rate six times higher than the global average during earthquakes. Theref...
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| Published in: | Journal of systems science and systems engineering Vol. 33; no. 1; pp. 1 - 29 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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Berlin/Heidelberg
Springer Berlin Heidelberg
01.02.2024
Springer Nature B.V |
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| ISSN: | 1004-3756, 1861-9576 |
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| Abstract | Pre-crisis management involves the optimal selection of relief and rescue centers to minimize vulnerability. Iran is particularly vulnerable due to its location on the Alpine-Himalaya seismic belt, resulting in an average death rate six times higher than the global average during earthquakes. Therefore, selecting appropriate relief and rescue centers is crucial to Iran’s disaster preparedness. When selecting the placement of rescue centers, accessibility and the appropriateness of the land should be taken into account as well as the distance from high-risk areas. The location of these centers does not require any particular combinations. To address this issue, a study was conducted utilizing GIS, artificial neural networks, fuzzy logic, and mathematical models to determine the optimal placement based on 12 indicators within two clusters: natural and human. To examine the information layers of the initial stage, a spatial data repository concerning the variables impacting the placement of these centers was established using ARCGIS. Using functions and algorithms such as Fuzzy Logic in IDRISI, TOPSIS, and VIKOR software, the layers were assessed for weightage before being overlaid. The study’s analysis of the models used revealed that the positioning priority limits of the areas differed across all four models. Notably, the areas with high desirability varied to a greater extent: the fuzzy model varied by 9.3%, neural network by 12.4%, VIKOR by 4.5%, and TOPSIS by 16.2%. The variance in results can be attributed to the differing levels of risk acceptance and non-acceptance in each model. Additionally, the study yielded other significant findings such as the correlation between study area size and model accuracy. Specifically, smaller study areas exhibited higher model accuracy. The research also demonstrated that both fuzzy and VIKOR models achieved greater accuracy. As a result, employing these models in crisis management planning, particularly in pre-crisis management for identifying rescue center locations, would be highly advantageous and increase the precision of these endeavors. |
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| AbstractList | Pre-crisis management involves the optimal selection of relief and rescue centers to minimize vulnerability. Iran is particularly vulnerable due to its location on the Alpine-Himalaya seismic belt, resulting in an average death rate six times higher than the global average during earthquakes. Therefore, selecting appropriate relief and rescue centers is crucial to Iran’s disaster preparedness. When selecting the placement of rescue centers, accessibility and the appropriateness of the land should be taken into account as well as the distance from high-risk areas. The location of these centers does not require any particular combinations. To address this issue, a study was conducted utilizing GIS, artificial neural networks, fuzzy logic, and mathematical models to determine the optimal placement based on 12 indicators within two clusters: natural and human. To examine the information layers of the initial stage, a spatial data repository concerning the variables impacting the placement of these centers was established using ARCGIS. Using functions and algorithms such as Fuzzy Logic in IDRISI, TOPSIS, and VIKOR software, the layers were assessed for weightage before being overlaid. The study’s analysis of the models used revealed that the positioning priority limits of the areas differed across all four models. Notably, the areas with high desirability varied to a greater extent: the fuzzy model varied by 9.3%, neural network by 12.4%, VIKOR by 4.5%, and TOPSIS by 16.2%. The variance in results can be attributed to the differing levels of risk acceptance and non-acceptance in each model. Additionally, the study yielded other significant findings such as the correlation between study area size and model accuracy. Specifically, smaller study areas exhibited higher model accuracy. The research also demonstrated that both fuzzy and VIKOR models achieved greater accuracy. As a result, employing these models in crisis management planning, particularly in pre-crisis management for identifying rescue center locations, would be highly advantageous and increase the precision of these endeavors. |
| Author | Souri, Mahmood Nayyeri, Hadi Zandi, Sahar |
| Author_xml | – sequence: 1 givenname: Hadi surname: Nayyeri fullname: Nayyeri, Hadi email: h.nayeri@uok.ac.ir organization: Department of Geomorphology, Faculty of Natural Resource, University of Kurdistan – sequence: 2 givenname: Sahar surname: Zandi fullname: Zandi, Sahar organization: Department of Geography Sciences, Kharazmi University – sequence: 3 givenname: Mahmood surname: Souri fullname: Souri, Mahmood organization: Red Crescent Society of the Islamic Republic Employee of Iran |
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| Cites_doi | 10.1007/s00477-022-02361-5 10.1029/01EO00271 10.29252/jgs.18.50.199 10.1016/0040-1951(94)00185-C 10.1016/0377-2217(94)90282-8 10.3390/land12010242 10.1007/978-3-030-47439-3 10.1016/j.cie.2015.05.036 10.1016/j.habitatint.2004.04.003 10.1109/2.485891 10.1016/j.ijdrr.2016.10.009 10.1016/j.scitotenv.2017.09.210 10.2136/sssaj2000.6451706x 10.1023/B:ANOR.0000030690.27939.39 10.1080/13658816.2013.763944 10.1002/atr.5670420405 10.1016/0191-2615(91)90037-J 10.1016/j.ijdrr.2022.103512 10.1016/j.jclepro.2022.131841 10.1016/j.gsf.2020.09.022 10.1111/j.1400-0952.2004.01068.x 10.1016/j.ijdrr.2015.12.006 10.1186/s40677-019-0119-7 10.1155/2021/5944828 10.1007/s10479-021-04191-0 10.1098/rsta.1974.0044 10.1080/10807039.2016.1185692 10.1016/j.ijdrr.2021.102668 10.1016/j.tecto.2003.09.020 10.1016/j.scitotenv.2019.07.203 10.1007/s10668-023-03356-0 10.1016/j.catena.2007.01.003 |
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| Copyright | Systems Engineering Society of China and Springer-Verlag GmbH Germany 2023 Systems Engineering Society of China and Springer-Verlag GmbH Germany 2023. |
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| SubjectTerms | Accuracy Algorithms Artificial neural networks Complexity Disaster management Economic Theory/Quantitative Economics/Mathematical Methods Emergency preparedness Engineering Fuzzy logic Management of crises Management planning Mathematical models Model accuracy Neural networks Operations Research/Decision Theory Placement Risk acceptance Risk levels Seismic response Spatial analysis Spatial data |
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