Cluster-Based Relocation of Stations for Efficient Forest Fire Management in the Province of Valencia (Spain)
1
Computer Science Department, Universitat de València, Av. de la Universitat s/n, 46100 Burjassot, Spain
2
I.U. Matemática Pura y Aplicada, Universitat Politècnica de València, Camino de Vera s/n, 46022 València, Spain
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Academic Editors: ByoungChul Ko and Sejin Park
Sensors 2021, 21(3), 797; https://doi.org/10.3390/s21030797
Received: 29 November 2020
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Revised: 20 January 2021
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Accepted: 21 January 2021
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Published: 25 January 2021
(This article belongs to the Special Issue Natural Disaster Prediction Based on Intelligent Sensor and Machine Learning)
Forest fires are undesirable situations with tremendous impacts on wildlife and people’s lives. Reaching them quickly is essential to slowing down their expansion and putting them out in an effective manner. This work proposes an optimized distribution of fire stations in the province of Valencia (Spain) to minimize the impacts of forest fires. Using historical data about fires in the Valencia province, together with the location information about existing fire stations and municipalities, two different clustering techniques have been applied. Floyd–Warshall dynamic programming algorithm has been used to estimate the average times to reach fires among municipalities and fire stations in order to quantify the impacts of station relocation. The minimization was done approximately through k-means clustering. The outcomes with different numbers of clusters determined a predicted tradeoff between reducing the time and the cost of more stations. The results show that the proposed relocation of fire stations generally ensures faster arrival to the municipalities compared to the current disposition of fire stations. In addition, deployment costs associated with station relocation are also of paramount importance, so this factor was also taken into account in the proposed approach.
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Keywords:
fire prevention; artificial intelligence; k-means; DBSCAN; Floyd–Warshall
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MDPI and ACS Style
de Domingo, M.; Ortigosa, N.; Sevilla, J.; Roger, S. Cluster-Based Relocation of Stations for Efficient Forest Fire Management in the Province of Valencia (Spain). Sensors 2021, 21, 797. https://doi.org/10.3390/s21030797
AMA Style
de Domingo M, Ortigosa N, Sevilla J, Roger S. Cluster-Based Relocation of Stations for Efficient Forest Fire Management in the Province of Valencia (Spain). Sensors. 2021; 21(3):797. https://doi.org/10.3390/s21030797
Chicago/Turabian Stylede Domingo, Miguel, Nuria Ortigosa, Javier Sevilla, and Sandra Roger. 2021. "Cluster-Based Relocation of Stations for Efficient Forest Fire Management in the Province of Valencia (Spain)" Sensors 21, no. 3: 797. https://doi.org/10.3390/s21030797
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