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Identifying wildland fire ignition factors through sensitivity analysis of a neural network

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  • Christos Vasilakos
  • Kostas Kalabokidis
  • John Hatzopoulos
  • Ioannis Matsinos

Abstract

Artificial neural networks (ANNs) show a significant ability to discover patterns in data that are too obscure to go through standard statistical methods. Data of natural phenomena usually exhibit significantly unpredictable non-linearity, but the robust behavior of a neural network makes it perfectly adaptable to environmental models such as a wildland fire danger rating system. These systems have been adopted by many developed countries that have invested in wildland fire prevention, and thus civil protection agencies are able to identify areas with high probabilities of fire ignition and resort to necessary actions. Since one of the drawbacks of ANNs is the interpretation of the final model in terms of the importance of variables, this article presents the results of sensitivity analysis performed in a back-propagation neural network (BPN) to distinguish the influence of each variable in a fire ignition risk scheme developed for Lesvos Island in Greece. Four different methods were utilized to evaluate the three fire danger indices developed within the above scheme; three of the methods are based on network’s weights after the training procedure (i.e., the percentage of influence—PI, the weight product—WP, and the partial derivatives—PD methods), and one is based on the logistic regression (LR) model between BPN inputs and observed outputs. Results showed that the occurrence of rainfall, the 10-h fuel moisture content, and the month of the year parameter are the most significant variables of the Fire Weather, Fire Hazard, and Fire Risk Indices, respectively. Relative humidity, elevation, and day of the week have a small contribution to fire ignitions in the study area. The PD method showed the best performance in ranking variables’ importance, while performance of the rest of the methods was influenced by the number of input parameters and the magnitude of their importance. The results can be used by local forest managers and other decision makers dealing with wildland fires to take the appropriate preventive measures by emphasizing on the important factors of fire occurrence. Copyright Springer Science+Business Media B.V. 2009

Suggested Citation

  • Christos Vasilakos & Kostas Kalabokidis & John Hatzopoulos & Ioannis Matsinos, 2009. "Identifying wildland fire ignition factors through sensitivity analysis of a neural network," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 50(1), pages 125-143, July.
  • Handle: RePEc:spr:nathaz:v:50:y:2009:i:1:p:125-143
    DOI: 10.1007/s11069-008-9326-3
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    References listed on IDEAS

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    1. Park, Young-Seuk & Rabinovich, Jorge & Lek, Sovan, 2007. "Sensitivity analysis and stability patterns of two-species pest models using artificial neural networks," Ecological Modelling, Elsevier, vol. 204(3), pages 427-438.
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    Cited by:

    1. Santos Daniel Chicas & Jonas Østergaard Nielsen, 2022. "Who are the actors and what are the factors that are used in models to map forest fire susceptibility? A systematic review," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 114(3), pages 2417-2434, December.
    2. Yen-Ming Chiang & Wei-Guo Cheng & Fi-John Chang, 2012. "A hybrid artificial neural network-based agri-economic model for predicting typhoon-induced losses," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 63(2), pages 769-787, September.
    3. Xiaowei Li & Gang Zhao & Xiubo Yu & Qiang Yu, 2014. "A comparison of forest fire indices for predicting fire risk in contrasting climates in China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 70(2), pages 1339-1356, January.
    4. Alessandra Canepa, 2024. "Socio-economic risk factors and wildfire crime in Italy: a quantile panel approach," Empirical Economics, Springer, vol. 66(1), pages 431-465, January.
    5. Hamed Adab & Kasturi Kanniah & Karim Solaimani, 2013. "Modeling forest fire risk in the northeast of Iran using remote sensing and GIS techniques," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 65(3), pages 1723-1743, February.
    6. Xu Jia & Yong Gao & Baocheng Wei & Shan Wang & Guodong Tang & Zhonghua Zhao, 2019. "Risk Assessment and Regionalization of Fire Disaster Based on Analytic Hierarchy Process and MODIS Data: A Case Study of Inner Mongolia, China," Sustainability, MDPI, vol. 11(22), pages 1-17, November.
    7. Saeedeh Eskandari & Mahdis Amiri & Nitheshnirmal Sãdhasivam & Hamid Reza Pourghasemi, 2020. "Comparison of new individual and hybrid machine learning algorithms for modeling and mapping fire hazard: a supplementary analysis of fire hazard in different counties of Golestan Province in Iran," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 104(1), pages 305-327, October.
    8. Canepa, Alessandra & Drogo, Federico, 2021. "Wildfire crime, apprehension and social vulnerability in Italy," Forest Policy and Economics, Elsevier, vol. 122(C).
    9. Sarkawt G. Salar & Arsalan Ahmed Othman & Sabri Rasooli & Salahalddin S. Ali & Zaid T. Al-Attar & Veraldo Liesenberg, 2022. "GIS-Based Modeling for Vegetated Land Fire Prediction in Qaradagh Area, Kurdistan Region, Iraq," Sustainability, MDPI, vol. 14(10), pages 1-31, May.
    10. Melania Michetti & Mehmet Pinar, 2019. "Forest Fires Across Italian Regions and Implications for Climate Change: A Panel Data Analysis," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 72(1), pages 207-246, January.
    11. Canepa,Alessandra & Drogo,Federico, 2019. "Wildfire Crime and Social Vulnerability in Italy: A Panel Investigation," Department of Economics and Statistics Cognetti de Martiis. Working Papers 202005, University of Turin.

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