Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11779/2403
Title: Fuzzy Elephant Herding Optimization and Dbscan for Emergency Transportation: a Case Study for the 2023 Türkiye Earthquake
Authors: Drias, Y.
Drias, H.
Keywords: Ambulance dispatching
Dbscan
Emergency transportation
Fuzzy elephant herding optimization
Publisher: Springer Science and Business Media Deutschland GmbH
Abstract: In recent times, our planet has experienced numerous natural disasters across all continents. The damage caused by these disasters has been so extensive that Emergency Medical Services (EMS) proved incapable of handling the situation. In this article, we present a novel approach for urgent disaster transport with the aim of minimizing loss of life. In this context, we are investigating the Density-Based Spatial Clustering of Applications with Noise algorithm (DBSCAN) to cluster the large geographic zone affected by the 2023 earthquake in Türkiye. The clustering is done based on hospitals’ capacity on one hand and damages on the other hand. The ambulance dispatching task is then tackled using a new fuzzy version of Elephant Herding Optimization called FEHO. This approach addresses the challenge of dispatching ambulances to cover emergency locations effectively and optimally in the clustered regions. Experiments conducted on real data demonstrate the effectiveness of our approach in managing emergency transportation and highlight its potential to minimize the number of casualties. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
URI: https://doi.org/10.1007/978-3-031-67195-1_47
https://hdl.handle.net/20.500.11779/2403
ISBN: 9783031671944
ISSN: 2367-3370
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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