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iForest - Biogeosciences and Forestry

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Analysis of factors influencing deployment of fire suppression resources in Spain using artificial neural networks

Sergi Costafreda-Aumedes (1), Adrian Cardil (2), Domingo M Molina (2), Sarah N Daniel (3), Robert Mavsar (4), Cristina Vega-Garcia (1)   

iForest - Biogeosciences and Forestry, Volume 9, Issue 1, Pages 138-145 (2015)
doi: https://doi.org/10.3832/ifor1329-008
Published: Jul 19, 2015 - Copyright © 2015 SISEF

Research Articles


In Spain, the established fire control policy states that all fires must be controlled and put out as soon as possible. Though budgets have not restricted operations until recently, we still experience large fires and we often face multiple-fire situations. Furthermore, fire conditions are expected to worsen in the future and budgets are expected to drop. To optimize the deployment of firefighting resources, we must gain insights into the factors affecting how it is conducted. We analyzed the national data base of historical fire records in Spain for patterns of deployment of fire suppression resources for large fires. We used artificial neural networks to model the relationships between the daily fire load, fire duration, fire type, fire size and response time, and the personnel and terrestrial and aerial units deployed for each fire in the period 1998-2008. Most of the models highlighted the positive correlation of burned area and fire duration with the number of resources assigned to each fire and some highlighted the negative influence of daily fire load. We found evidence suggesting that firefighting resources in Spain may already be under duress in their compliance with Spain’s current full suppression policy.

  Keywords


Fire Management, Neural Networks, Regional Models, Suppression Resources, Wildfire

Authors’ address

(1)
Sergi Costafreda-Aumedes
Cristina Vega-Garcia
Agriculture and Forest Engineering Department, University of Lleida, Alcalde Rovira Roure 191, 25198, Lleida (Spain)
(2)
Adrian Cardil
Domingo M Molina
Department of Crop and Forest Sciences, University of Lleida, Alcalde Rovira Roure 191, 25198, Lleida (Spain)
(3)
Sarah N Daniel
Agriculture and Environment Department, YMCA-Lebanon, Delta Center, 3rd floor - Horsh Tabet, Sin El Fil, 55570, Beirut (Lebanon)
(4)
Robert Mavsar
European Forest Institute (EFI), Yliopistokatu 6, 80100 Joensuu (Finland)

Corresponding author

 
Cristina Vega-Garcia
cvega@eagrof.udl.es

Citation

Costafreda-Aumedes S, Cardil A, Molina DM, Daniel SN, Mavsar R, Vega-Garcia C (2015). Analysis of factors influencing deployment of fire suppression resources in Spain using artificial neural networks. iForest 9: 138-145. - doi: 10.3832/ifor1329-008

Academic Editor

Davide Ascoli

Paper history

Received: Apr 29, 2014
Accepted: Mar 24, 2015

First online: Jul 19, 2015
Publication Date: Feb 21, 2016
Publication Time: 3.90 months

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(1)
Ager AA, Vaillant NM, McMahan A (2013)
Restoration of fire in managed forests: a model to prioritize landscapes and analyze tradeoffs. Ecosphere 4: art29.
CrossRef | Gscholar
(2)
Alcázar J, Palau A, Vega-Garcia C (2008)
A neural net model for environmental flow estimation at the Ebro River Basin, Spain. Journal of Hydrology 349: 44-55.
CrossRef | Gscholar
(3)
Alexander ME, Cruz MG (2014)
Chapter 9: crown fire dynamics in conifer forests. In: “Synthesis of Knowledge of Extreme Fire Behavior: Volume 2 for Fire Behavior Specialists, Researchers and Meteorologists”. General Technical Report PNW-GTR, Pacific Northwest Research Station, USDA Forest Service, Portland, OR, USA. [in press]
Gscholar
(4)
Alonso-Betanzos A, Fontenla-Romero O, Guijarro-Berdiñas B, Hernández-Pereira E, Paz Andrade MI, Andrade P, Jiménez E, Legido Soto JL, Carballas T (2003)
An intelligent system for forest fire risk prediction and fire fighting management in Galicia. Expert Systems with Applications 25: 545-554.
CrossRef | Gscholar
(5)
Calkin DE, Thompson MP, Finney MA, Hyde KD (2011)
A real-time risk-assessment tool supporting wildland fire decision making. Journal of Forestry 109: 274-280.
Online | Gscholar
(6)
Cardil A, Molina DM (2013)
Large wildland fires in three diverse regions in Spain from 1978 to 2010. Forest Systems 22: 526-534.
CrossRef | Gscholar
(7)
Cardil A, Salis M, Spano D, Delogu G, Molina Terrén D (2014)
Large wildland fires and extreme temperatures in Sardinia (Italy). iForest 7 (3): 162-169.
CrossRef | Gscholar
(8)
Cassagne N, Pimont F, Dupuy J-L, Linn RR, Marell A, Oliveri C, Rigolot E (2011)
Using a fire propagation model to assess the efficiency of prescribed burning in reducing the fire hazard. Ecological Modelling 222: 1502-1514.
CrossRef | Gscholar
(9)
Castellnou M, Larrañaga A, Miralles M, Molina DM (2010)
Improving wildfire scenarios: learning from experience. Research Report No. 23, European Commission, EFI, Joensuu, Finland, pp. 121-133.
Gscholar
(10)
Chas-Amil ML, Touza J, Prestemon JP (2010)
Spatial distribution of human-caused forest fires in Galicia (NW Spain). WIT Transactions on Ecology and the Environment 137: 247-258.
Online | Gscholar
(11)
Corne SA, Carver SJ, Kunin WE, Lennon JJ, van Hees WWS (2004)
Predicting forest attributes in Southeast Alaska using artificial neural networks. Forest Science 50: 259-276.
Online | Gscholar
(12)
Debouk H, Riera-Tatche R, Vega-Garcia C (2013)
Assessing post-fire regeneration in a Mediterranean mixed forest using LIDAR data and Artificial Neural Networks. Photogrammetric Engineering and Remote Sensing 79: 11-21.
CrossRef | Gscholar
(13)
Dimuccio LA, Ferreira R, Cunha L, Campar de Almeida A (2011)
Regional forest-fire susceptibility analysis in central Portugal using a probabilistic ratings procedure and artificial neural network weights assignment. International Journal of Wildland Fire 20: 776-791.
CrossRef | Gscholar
(14)
Donovan GH, Rideout DB (2003)
A reformulation of the cost plus net value change (C+NVC) model of wildfire economics. Forest Science 49: 318-323.
Online | Gscholar
(15)
Dupuy JL (2009)
Fire start and spread. In: “Living with Wildfires: What Science Can Tell Us” (Birot Y ed). Discussion paper 15, European Forest Institute, Joensuu, Finland, pp. 27-31.
Gscholar
(16)
Fahlman SE, Lebiere C (1990)
The cascade-correlation learning architecture. In: “Advances in Neural Information Processing Systems. 2” (Touretzky ed), Morgan Kaufmann, S. Francisco, CA, USA, pp. 524-532 -
Online | Gscholar
(17)
FAO (2013)
State of Mediterranean Forests 2013. FAO, Rome, Italy, pp. 174.
Online | Gscholar
(18)
Fernandes PM, Davies GM, Ascoli D, Fernández C, Moreira F, Rigolot E, Stoof CR, Vega JA, Molina D (2013)
Prescribed burning in southern Europe: developing fire management in a dynamic landscape. Frontiers in Ecology and the Environment 11 (s1): e4-e14.
CrossRef | Gscholar
(19)
Finney MA, Grenfell IC, McHugh CW (2009)
Modeling containment of large wildfires using generalized lineal mixed-model analysis. Forest Science 55: 249-255.
Online | Gscholar
(20)
Ganewatta G, Handmer J (2009)
The cost effectiveness of aerial fire fighting in Australia”. Technical Report No. A.09.01, Bushfire Cooperative Research Center CRC, Melbourne Australia.
Gscholar
(21)
García-Rey M, Ramírez V, Jarreta D (2014)
Opacidad y recortes en el gasto contra los incendios forestales [Opacity and cuts in spending against forest fires]. Espana en Llamas, web site report.
Online | Gscholar
(22)
Gebert KM, Black AE (2012)
Effect of suppression strategies on federal wildland fire expenditures. Journal of Forestry 110: 65-73.
CrossRef | Gscholar
(23)
Gebert KM, Calkin DE, Yoder J (2007)
Estimating suppression expenditures for individual large wildland fires. Western Journal of Applied Forestry 22: 188-192.
Online | Gscholar
(24)
Gonzalez-Caban A (1986)
Developing fire management mixes for fire program planning. Gen. Tech. Rep. PSW-88, Pacific Southwest Forest and Range Experiment Station, USDA Forest Service, Berkeley, CA, USA, pp. 8.
Online | Gscholar
(25)
Guan BT, Gertner G (1991)
Modeling red pine tree survival with an artificial neural network. Forest Science 37: 1429-1440.
Online | Gscholar
(26)
Hasenauer H, Merkl D, Weingartner M (2001)
Estimating tree mortality of Norway spruce stands with neural networks. Advances in Environmental Research 5: 405-414.
CrossRef | Gscholar
(27)
Hilbert DW, Ostendorf B (2001)
The utility of artificial neural networks for modelling the distribution of vegetation in past, present and future climates. Ecological Modelling 146: 311-327.
CrossRef | Gscholar
(28)
Hunter TP (1981)
Initial attack time and the influence of multiple fires. In: Proceedings of the Meeting “Computer modelling: its application in fire management” (Lotan JE ed). Salt Lake City (UT, USA) 20-22 Oct 1981. Intermountain Fire Council, Logan, UT, USA pp. 157-173.
Gscholar
(29)
Islam KMS, Martell DL (1998)
Performance of initial attack airtanker systems with interacting bases and variable initial attack ranges. Canadian Journal of Forest Research 28: 1448-1455.
CrossRef | Gscholar
(30)
Jutras P, Prasher SO, Mehuys GR (2009)
Prediction of street tree morphological parameters using artificial neural networks. Computers and Electronics in Agriculture 67: 9-17.
CrossRef | Gscholar
(31)
Karouni A, Daya B, Chauvet P (2014)
Applying decision tree algorithm and neural networks to predict forest fires in Lebanon. Journal of Theoretical and Applied Information Technology 63: 282-291.
Gscholar
(32)
Kaval P (2009)
Perceived and actual wildfire danger: an economic and spatial analysis study in Colorado (USA). Journal of Environmental Management 234: 1-9.
CrossRef | Gscholar
(33)
Li LM, Song WG, Ma J, Satoh K (2009)
Artificial Neural Network approach for modelling the impact of population density and weather parameters on forest fire risk. International Journal of Wildland Fire 18: 640-667.
CrossRef | Gscholar
(34)
Liang J, Calkin DE, Gebert KM, Venn TJ, Silverstein RP (2008)
Factors influencing large wildland fire suppression expenditures. International Journal of Wildland Fire 17: 650-659.
CrossRef | Gscholar
(35)
Martin-Fernandez F, Martinez-Falero E, Perez JM (2002)
Optimization of resources management in wildland fire suppression expenditures. International Journal of Wildland Fire 17: 650-659.
Gscholar
(36)
Mees RM, Strauss D (1992)
Allocating resources to large wildland fires: a model with stochastic production rates. Forest Science 38: 842-853. [online]
Online | Gscholar
(37)
Mendes I (2010)
A theoretical economic model for choosing efficient wildfire suppression strategies. Forest Policy and Economics 12: 329-329.
CrossRef | Gscholar
(38)
Mills TJ, Bratten FW (1988)
Economic efficiency and risk character of fire programs, northern Rocky Mountains. Research Paper PSW-192, Pacific Southwest Forest and Range Experiment Station, USDA Forest Service, Berkeley, USA, pp. 52.
Online | Gscholar
(39)
Mitrakis NE, Mallinis G, Koutsias N, Theocharis JB (2012)
Burned area mapping in Mediterranean environment using medium-resolution multi-spectral data and a neuro-fuzzy classifier. International Journal of Image and Data Fusion 3: 299-318.
CrossRef | Gscholar
(40)
Molina DM, Castellnou M, Garcia D, Salgueiro A (2010)
Improving fire management success through fire behavior specialists. In: EFI Research Report no. 23 “Fire Paradox”, Project no. FP6-018505, European Commission, Joensuu, Finland, pp. 105-119.
Online | Gscholar
(41)
Moreno MV, Chuvieco E (2002)
Characterising fire regimes in Spain from fire statistics. International Journal of Wildland Fire 22: 296-305.
CrossRef | Gscholar
(42)
Moreno MV, Conedera M, Chuvieco E, Pezzatti GB (2014)
Fire regime changes and major driving forces in Spain from 1968 to 2010. Environmental Science and Policy 37: 11-22.
CrossRef | Gscholar
(43)
NeuralWare (2009)
NeuralWorks Predict, the complete solution for neural data modelling. User Guide, version 3.24, NeuralWare, Carnergie, PA, USA, pp. 397.
Gscholar
(44)
Padilla M, Vega-Garcia C (2011)
On the comparative importance of fire danger rating indices and their integration with spatial and temporal variables for predicting daily human-caused fire occurrences in Spain. International Journal of Wildland Fire 20: 46-58.
CrossRef | Gscholar
(45)
Paveglio TB, Carroll MS, Jakes PJ (2010)
Alternatives to evacuation during wildland fire: exploring adaptive capacity in one Idaho community. Environmental Hazards: Human and Policy Dimensions 9: 379-394.
Online | Gscholar
(46)
Rachaniotis NP, Pappis CP (2006)
Scheduling fire-fighting tasks using the concept of “deteriorating jobs”. Canadian Journal of Forest Research 36: 652-658.
CrossRef | Gscholar
(47)
Raftoyannis Y, Nocentini S, Marchi E, Calama Sainz R, Garcia Guemes C, Pilas I, Peric S, Amaral Paulo J, Moreira-Marcelino AC, Costa-Ferreira M, Kakouris E, Lindner M (2014)
Perceptions of forest experts on climate change and fire management in European Mediterranean forests. iForest 7: 33-41.
CrossRef | Gscholar
(48)
Rodriguez-Silva F (2007)
Coste y eficiencia en las operaciones de extinción de los incendios forestales, fundamentos y herramientas para su estudio y análisis [Cost-efficiency in wildland FIRE management]. In: Proceedings of the “4th International Wildland Fire Conference - Wildfire 2007”. Sevilla (Spain) 13-17 May 2007. Organismo Autónomo de Parques Nacionales, Ministerio de Medio Ambiente, Madrid, Spain.
Gscholar
(49)
Ruiz-Mirazo J, Martínez-Fernández J, Vega-García C (2012)
Pastoral wildfires in the Mediterranean: understanding their linkages to land cover patterns in managed landscapes. Journal of environmental management 98: 43-50.
CrossRef | Gscholar
(50)
Scrinzi G, Marzullo L, Galvagni D (2007)
Development of a neural network model to update forest distribution data for managed alpine stands. Ecological Modelling 206: 331-346.
CrossRef | Gscholar
(51)
Simard AJ, Young A (1978)
AIRPRO - an air tanker productivity computer simulation model - application. Ontario Information Report FF-X-69, Forest Fire Research Institute, Canadian Forest Service, Ottawa, Canada, pp. 23.
Gscholar
(52)
Vasconcelos MJP, Silva S, Tome M, Alvim M, Pereira JMC (2001)
Spatial prediction of fire ignition probabilities: comparing logistic regression and neural networks. Photogrammetric Engineering and Remote Sensing 67: 73-81.
Online | Gscholar
(53)
Vasilakos C, Kalabokidis K, Hatzopoulos J, Matsinnos I (2009)
Identifying wildland fire ignition factors through sensitivity analysis of a neural network. Natural Hazards 50: 125-143.
CrossRef | Gscholar
(54)
Vázquez de la Cueva A, García del Barrio JM, Ortega Quero M, Sánchez Palomares O (2006)
Recent fire regime in peninsular Spain in relation to forest potential productivity and population density. International Journal of Wildland Fire 15: 397-405.
CrossRef | Gscholar
(55)
Vega-Garcia C, Lee BS, Woodard PM, Titus SJ (1996)
Applying neural network technology to human-caused wildfire occurrence prediction. AI Applications 10: 9-18.
Gscholar
(56)
Vega-Garcia C, Chuvieco E (2006)
Applying local measures of spatial heterogeneity to Landsat-TM images for predicting wildfire occurrence in Mediterranean landscapes. Landscape Ecology 21: 595-605.
CrossRef | Gscholar
(57)
Velez R (2000)
La defensa contra incendios forestales: fundamentos y experiencias [Wildland fire management: principles and experience]. McGraw-Hill publishing, Madrid, Spain, pp. 800. [in Spanish]
Gscholar
(58)
Vélez R (2009)
Forest fires in the Mediterranean Basin. In: Proceedings of the “3rd International Symposium on Fire Economics, Planning and Policy: Common Problems and Approaches” (González-Cabán A ed). General Technical Report PSW-GTR-227, Pacific Southwest Research Station, USDA Forest Service, Davis, CA, USA, pp. 1-7.
Online | Gscholar
(59)
Verdu F, Salas J, Vega-Garcia C (2012)
A multivariate analysis of biophysical factors and forest fires in Spain, 1991-2005. International Journal of Wildland Fire 21: 498-509.
CrossRef | Gscholar
(60)
Werbos PJ (1994)
The roots of backpropagation: from ordered derivatives to neural networks and political forecasting. John Wiley & Sons Inc, New York, USA, pp. 319.
Online | Gscholar
 

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