Machine Learning for Earthquake Emergency Evacuation: Site Selection and Neighborhood Navigation
This research is first to introduce a machine learning-based method to enhance the quality and speed of selecting emergency evacuation centers in Tehran, optimizing the use of the city’s current capacities. Tehran, with a population of 8.7 million, is located on multiple active faults, wh...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11014087/ |
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| author | Amirmasoud Amiran Behrouz Behnam Sanaz Seyedin |
| author_facet | Amirmasoud Amiran Behrouz Behnam Sanaz Seyedin |
| author_sort | Amirmasoud Amiran |
| collection | DOAJ |
| description | This research is first to introduce a machine learning-based method to enhance the quality and speed of selecting emergency evacuation centers in Tehran, optimizing the use of the city’s current capacities. Tehran, with a population of 8.7 million, is located on multiple active faults, where 258 out of its 373 neighborhoods lack sufficient emergency evacuation centers. This inadequacy poses a significant risk during emergencies, potentially leading to failures in the evacuation process. By employing an artificial neural network model informed by data from San Francisco—a city with similar seismic risks, urban structure, building types, and population density—this study achieved a 23% reduction in the average minimum distance from each parcel to the nearest emergency evacuation center compared to the current conditions. Additionally, per capita adequacy at the neighborhood level increased from 27% to 59%. The second goal of the research is to streamline the emergency evacuation process through the development of an application that dynamically navigates users to the nearest evacuation center identified by the model. This application is built using OpenStreetMap and the OpenRouteService routing algorithm. With the successful achievement of both goals, the emergency evacuation process in Tehran is now smarter and more comprehensive. The model here can also be applied to other cities that are similar to the cities studied in terms of site-selecting criteria. For cities with less similarity, the model needs to be slightly trained with the data of the target city to achieve desirable results. |
| format | Article |
| id | doaj-art-0f52bd2d3c90469c9afdc6742ef4b0a1 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-0f52bd2d3c90469c9afdc6742ef4b0a12025-08-20T02:19:31ZengIEEEIEEE Access2169-35362025-01-0113915959161010.1109/ACCESS.2025.357320711014087Machine Learning for Earthquake Emergency Evacuation: Site Selection and Neighborhood NavigationAmirmasoud Amiran0https://orcid.org/0000-0003-3100-295XBehrouz Behnam1https://orcid.org/0000-0002-8348-4711Sanaz Seyedin2https://orcid.org/0000-0002-3626-7589Department of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, IranDepartment of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran, IranDepartment of Electrical Engineering, Amirkabir University of Technology, Tehran, IranThis research is first to introduce a machine learning-based method to enhance the quality and speed of selecting emergency evacuation centers in Tehran, optimizing the use of the city’s current capacities. Tehran, with a population of 8.7 million, is located on multiple active faults, where 258 out of its 373 neighborhoods lack sufficient emergency evacuation centers. This inadequacy poses a significant risk during emergencies, potentially leading to failures in the evacuation process. By employing an artificial neural network model informed by data from San Francisco—a city with similar seismic risks, urban structure, building types, and population density—this study achieved a 23% reduction in the average minimum distance from each parcel to the nearest emergency evacuation center compared to the current conditions. Additionally, per capita adequacy at the neighborhood level increased from 27% to 59%. The second goal of the research is to streamline the emergency evacuation process through the development of an application that dynamically navigates users to the nearest evacuation center identified by the model. This application is built using OpenStreetMap and the OpenRouteService routing algorithm. With the successful achievement of both goals, the emergency evacuation process in Tehran is now smarter and more comprehensive. The model here can also be applied to other cities that are similar to the cities studied in terms of site-selecting criteria. For cities with less similarity, the model needs to be slightly trained with the data of the target city to achieve desirable results.https://ieeexplore.ieee.org/document/11014087/Artificial intelligenceearthquake disaster managementemergency evacuation centersnavigator |
| spellingShingle | Amirmasoud Amiran Behrouz Behnam Sanaz Seyedin Machine Learning for Earthquake Emergency Evacuation: Site Selection and Neighborhood Navigation IEEE Access Artificial intelligence earthquake disaster management emergency evacuation centers navigator |
| title | Machine Learning for Earthquake Emergency Evacuation: Site Selection and Neighborhood Navigation |
| title_full | Machine Learning for Earthquake Emergency Evacuation: Site Selection and Neighborhood Navigation |
| title_fullStr | Machine Learning for Earthquake Emergency Evacuation: Site Selection and Neighborhood Navigation |
| title_full_unstemmed | Machine Learning for Earthquake Emergency Evacuation: Site Selection and Neighborhood Navigation |
| title_short | Machine Learning for Earthquake Emergency Evacuation: Site Selection and Neighborhood Navigation |
| title_sort | machine learning for earthquake emergency evacuation site selection and neighborhood navigation |
| topic | Artificial intelligence earthquake disaster management emergency evacuation centers navigator |
| url | https://ieeexplore.ieee.org/document/11014087/ |
| work_keys_str_mv | AT amirmasoudamiran machinelearningforearthquakeemergencyevacuationsiteselectionandneighborhoodnavigation AT behrouzbehnam machinelearningforearthquakeemergencyevacuationsiteselectionandneighborhoodnavigation AT sanazseyedin machinelearningforearthquakeemergencyevacuationsiteselectionandneighborhoodnavigation |