Abstract
Forest fires cause severe damages in ecosystems, human lives and infrastructure globally. This situation tends to get worse in the next decades due to climate change and the expected increase in the length and severity of the fire season. Thus, the ability to develop a method that reliably models the risk of fire occurrence is an important step towards preventing, confronting and limiting the disaster. Different approaches building upon Machine Learning (ML) methods for predicting wildfires and deriving a better understanding of fires’ regimes have been devised. This study demonstrates the development of a Random Forest (RF) classifier to predict “fire”/“non fire” classes in Greece. For this a prototype and representative for the Mediterranean ecosystem database of validated fires and fire related features has been created. The database is populated with data (e.g. Earth Observation derived biophysical parameters and daily collected climatic and weather data) for a period of nine years (2010–2018). Spatially it refers to grid cells of 500 m wide where Active Fires (AF) and Burned Areas/Burn Scars (BSM) were reported during that period. By using feature ranking techniques as Chi-squared and Spearman correlations the study showcases the most significant wildfire triggering variables. It also highlights the extent by which the database and selected features scheme can be used to successfully train a RF classifier for deriving “fire”/“non-fire” predictions over the country of Greece in the prospect of generating a dynamic fire risk system for daily assessments.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
The Copernicus Emergency Management Service Monitors Impact of Fires in Australia | Copernicus Emergency Management Service. https://emergency.copernicus.eu/mapping/ems/copernicus-emergency-management-service-monitors-impact-fires-autralia. Accessed 31 July 2020
European Commission: JRC Tecnical Report Forest Fires in Europe, Middle East and North Africa 2018 (2018)
Castellari, S., Kurnik, B.: Climate change, impacts and vulnerability in Europe 2016, no. 1. (2017)
Fares, S., et al.: Characterizing potential wildland fire fuel in live vegetation in the Mediterranean region. 74, 1 (2017). https://doi.org/10.1007/s13595-016-0599-5
Lambert, J., Drenou, C., Denux, J.-P., Balent, G., Cheret, V.: Monitoring forest decline through remote sensing time series analysis. GISci. Remote Sens. 50(4), 437–457 (2013). https://doi.org/10.1080/15481603.2013.820070
Pastor, E., Zárate, L., Planas, E., Arnaldos, J.: Mathematical models and calculation systems for the study of wildland fire behaviour. Progress Energy Combust. Sci. 29(2), 139–153 (2003). https://doi.org/10.1016/S0360-1285(03)00017-0.
Hong, H., Tsangaratos, P., Ilia, I., Liu, J., Zhu, A.X., Xu, C.: Applying genetic algorithms to set the optimal combination of forest fire related variables and model forest fire susceptibility based on data mining models. The case of Dayu County, China. Sci. Total Environ. 630, 1044–1056 (2018). https://doi.org/10.1016/j.scitotenv.2018.02.278
Jain, P., Coogan, S.C.P., Subramanian, S.G., Crowley, M., Taylor, S., Flannigan, M.D.: A review of machine learning applications in wildfire science and management (2020)
Pourtaghi, Z.S., Pourghasemi, H.R., Aretano, R., Semeraro, T.: Investigation of general indicators influencing on forest fire and its susceptibility modeling using different data mining techniques. Ecol. Indic. 64, 72–84 (2016). https://doi.org/10.1016/j.ecolind.2015.12.030
Kontoes, C., Keramitsoglou, I., Papoutsis, I., Sifakis, N., Xofis, P.: National scale operational mapping of burnt areas as a tool for the better understanding of contemporary wildfire patterns and regimes. Sensors 13(8), 11146–11166 ( 2013). https://doi.org/10.3390/s130811146
ΕΜΥ: Εθνική Μετεωρολογική Υπηρεσία. https://www.emy.gr/emy/el/. Accessed 31 July 2020
EEA: State of the environment report (SOER) No 1/2010 : The European environment: State and outlook 2010. Synthesis (2010)
Rivera, A., Bravo, C., Buob, G.: Climate Change and Land Ice (2017)
Kailidis, D., Karanikola, P.: Forest Fires 1900–2000. Giahoudi Press, Thessaloniki (2004)
Forest Fires in Europe 2006 | EU Science Hub. https://ec.europa.eu/jrc/en/publication/eur-scientific-and-technical-research-reports/forest-fires-europe-2006. Accessed 13 Aug 2020
Beyond Centre of Excellence for EO based monitoring of Natural Disasters. https://www.beyond-eocenter.eu/. Accessed 31 July 2020
Kontoes, C., Papoutsis, I., Themistocles, H., Ieronymidi, E., Keramitsoglou, I.: Remote Sensing Techniques for Forest Fire Disaster Management: The FireHub Operational Platform, Book Chapter No. 6, Integrating Scale in Remote Sensing and GIS (2017)
SEVIRI Monitor - NOA GIS. https://195.251.203.238/seviri/. Accessed 31 July 2020
Massada, A.B., Syphard, A.D., Stewart, S.I., Radeloff, V.C.: Wildfire ignition-distribution modelling: a comparative study in the Huron-Manistee National Forest, Michigan, USA (2013). https://doi.org/10.1071/WF11178
Killough, B.: Overview of the open data cube initiative. In: International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2018-July, pp. 8629–8632 (2018). https://doi.org/10.1109/IGARSS.2018.8517694
Chandrashekar, G., Sahin, F.: A survey on feature selection methods. Comput. Electr. Eng. 40(1), 16–28 (2014). https://doi.org/10.1016/j.compeleceng.2013.11.024
Bommert, A., Sun, X., Bischl, B., Rahnenführer, J., Lang, M.: Benchmark for filter methods for feature selection in high-dimensional classification data. Comput. Stat. Data Anal. 143(September) (2020). https://doi.org/10.1016/j.csda.2019.106839
Dodge, Y.: The Concise Encyclopedia of Statistics, p. 502. Springer, Heidelberg (2010)
Feelders, A., Verkooijen, W.: On the Statistical Comparison of Inductive Learning Methods (1996)
Bradley, A.P.: The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recogn. 30(7), 1145–1159 ( 1997). https://doi.org/10.1016/S0031-3203(96)00142-2
Ho, T.K.: Random decision forests. In: Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, vol. 1, pp. 278–282 (1995). https://doi.org/10.1109/ICDAR.1995.598994
Stone, M.: Cross-Validatory Choice and Assessment of Statistical Predictions (1974)
Tonini, M., D’andrea, M., Biondi, G., Esposti, S.D., Trucchia, A., Fiorucci, P.: A machine learning-based approach for wildfire susceptibility mapping. The case study of the Liguria region in Italy. Geoscience 10(3), 18 (2020). https://doi.org/10.3390/geosciences10030105
Bergstra, J., Ca, J.B., Ca, Y.B.: Random Search for Hyper-Parameter Optimization Yoshua Bengio (2012)
Kent, A., Berry, M.M., Luehrs, F.U., Perry, J.W.: Machine literature searching VIII. Operational criteria for designing information retrieval systems. Am. Doc. 6(2), 93–101 ( 1955). https://doi.org/10.1002/asi.5090060209
Acknowledgements
This paper has been supported by using data and resources from the following Projects funded from EC and the Greek Government - Ministry of Development & Investments : (1) FRAMEWORK SERVICE CONTRACT FOR COPERNICUS EMERGENCY MANAGEMENT SERVICE RISK AND RECOVERY MAPPING- The European Forest Fire Information System (EFFIS) JRC/IPR/2014/G.2/0012/OC; (2) FRAMEWORK SERVICE CONTRACT FOR COPERNICUS EMERGENCY MANAGEMENT SERVICE RISK AND RECOVERY MAPPING - Program Call for tender JRC/IPR/2014/G.2/0012/OC; and (3) CLIMPACT: Flagship Initiative for Climate Change and its Impact by the Hellenic Network of Agencies for Climate Impact Mitigation and Adaptation.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Apostolakis, A., Girtsou, S., Kontoes, C., Papoutsis, I., Tsoutsos, M. (2021). Implementation of a Random Forest Classifier to Examine Wildfire Predictive Modelling in Greece Using Diachronically Collected Fire Occurrence and Fire Mapping Data. In: Lokoč, J., et al. MultiMedia Modeling. MMM 2021. Lecture Notes in Computer Science(), vol 12573. Springer, Cham. https://doi.org/10.1007/978-3-030-67835-7_27
Download citation
DOI: https://doi.org/10.1007/978-3-030-67835-7_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-67834-0
Online ISBN: 978-3-030-67835-7
eBook Packages: Computer ScienceComputer Science (R0)