Next Article in Journal
Using Different Combinations of Body-Mounted IMU Sensors to Estimate Speed of Horses—A Machine Learning Approach
Previous Article in Journal
Learning-Based End-to-End Path Planning for Lunar Rovers with Safety Constraints
Previous Article in Special Issue
Light-Weight Student LSTM for Real-Time Wildfire Smoke Detection
Article

Cluster-Based Relocation of Stations for Efficient Forest Fire Management in the Province of Valencia (Spain)

by 1,*, 1,2,†, 1,† and 1,*,†
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 / Revised: 20 January 2021 / Accepted: 21 January 2021 / Published: 25 January 2021
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. View Full-Text
Keywords: fire prevention; artificial intelligence; k-means; DBSCAN; Floyd–Warshall fire prevention; artificial intelligence; k-means; DBSCAN; Floyd–Warshall
Show Figures

Figure 1

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 Style

de 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

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop