Forecasting the Spatial Behavior of a Forest Fire at Uncertainty and Instability of the Process

Complete text of the article:

Download article (pdf, 0.8MB )

UDС

614.841.42

DOI:

10.37482/0536-1036-2021-1-20-34

Abstract

The Russian forest fund, being a public domain of the people and a special kind of federal property, requires sustainable management at the national level. One of the key principles of forest management is to ensure that forests are conserved and protected against a wide range of threats, primarily forest fires. Although forest fires are a natural component of forest ecosystems and cannot be completely eliminated, researchers have currently revealed a decrease in the regulatory function and an increase in the destructive function of forest fires. Understanding the interrelations between the environmental factors and forest fire history is necessary for the development of effective and scientifically sound forest safety plans. The main purpose of the study is to increase the efficiency of the formation of an operational forecast of a forest fire in difficult conditions of a real fire (at instability and uncertainty). The author analyzed statistical data on forest fires the USA, Canada, Russia and the five southern European Union member states (Portugal, Spain, France, Italy and Greece) and confirmed the conclusion on the increase in the frequency of large forest fires. The most widely used in practice forecasting models of forest fire dynamics (Van Wagner, Rothermel, Finney, Cruz, etc.) and their computer implementations (Prometheus, FlamMap, FARSITE, VISUAL-SEVEIF, WILDFIRE ANALYST) are presented in the article. It is proposed to develop an intelligent system designed to create an operational forecast of a forest fire using convolutional neural networks (CNN). The structure of this system is described. It includes three main subsystems: information, intelligent and user interface. A key element of the intelligent subsystem is a forest fire propagation model, which recognizes data from sequential images, predicts the forest fire dynamics, and generates an image with a fire spread forecast. The scheme of the proposed model is described. It includes the following stages: data input; preprocessing of input data; recognition of objects using CNNs; forecasting the forest fire dynamics; output of operational forecast. The implementation features of the stage “recognition of objects using CNNs” are presented in detail: core size for each convolutional layer 3×3, activation function ReLu(x), filter in 2×2 pooling layers with step 2, max-pooling method, Object recognition and Semantic segmentation methods at the networks output.

Authors

Tatiana S. Stankevich, Candidate of Engineering; ResearcherID: O-7418-2017, ORCID: https://orcid.org/0000-0002-8707-7187

Affiliation

Kaliningrad State Technical University, Sovetskiy prosp., 1, Kaliningrad, 236022, Russian Federation; e-mail: tatiana.stankevich@klgtu.ru

Keywords

forest, forest fire, operational forecast, uncertainty, instability, convolutional neural networks, intelligent system

For citation

Stankevich T.S. Forecasting the Spatial Behavior of a Forest Fire at Uncertainty and Instability of the Process. Lesnoy Zhurnal [Russian Forestry Journal], 2021, no. 1, pp. 20–34. DOI: 10.37482/0536-1036-2021-1-20-34

References

1. State Standard. GOST 17.6.1.01–83. Nature Protection. Forest Protection and Preservation. Terms and Definitions. Moscow, Izdatel’stvo standartov, 2002, 6 р.
2. State Standard. GOST 12.3.046–91. Occupational Safety Standards System. Automatic Fire Fighting Systems. General Technical Requirements. Moscow, Izdatel’stvo standartov, 2002. 4 p.
3. State Standard RF ISO/IEC. GOST R ISO/IEC 9126–93. Information Technology. Software Product Evaluation. Quality Characteristics and Guidelines for Their Use. Moscow, Izdatel’stvo standartov, 2004. 10 p.
4. Grishin A.M. Math ematical Modeling of Forest Fires and New Fire Fighting Techniques. Novosibirsk, Nauka Publ., 1992. 405 p.
5. Website of the Unified Interdepartmental Statistical Information System (UISIS). Режим доступа: https://fedstat.ru (дата обращения: 06.11.19).
6. Katayeva L.Yu. Analysis of Dynamic Processes of Natural and Technogenic Emergency Situations: Dr. Phys.-Math. Sci. Diss. Nizhny Novgorod, 2009. 328 p.
7. Constitution of the Russian Federation Dated December 12, 1993. Режим доступа: http://www.consultant.ru/document/cons_doc_LAW_28399/ (дата обращения: 06.11.19). 
8. Forest Code of the Russian Federation Dated December 4, 2006: the Federal Law No. 200-FZ. Режим доступа: http://www.consultant.ru/document/cons_doc_LAW_64299/ (дата обращения: 06.11.19).
9. Maslennikov D.A. Features of Mathematical Modeling of Radiant Heat Flux Propagation from Burning Source in Case of Forest Fires on Heterogeneous Relief: Cand. Phys.-Math. Sci. Diss. Nizhny Novgorod, 2012. 109 p.
10. Russia’s Report on the Montreal Process. Criteria and Indicators for the Conservation and Sustainable Management of Temperate and Boreal Forests. 2003. Режим доступа: https://www.montrealprocess.org/documents/publications/general/2003/RussiaR/main.html#_Toc45611963 (дата обращения: 06.11.19).
11. The Answer of the Federal Agency for Forestry No. NK-09-50/8211 Dated May 13, 2019.
12. Perminov V.A. Mathematical Modeling of the Occurrence of Crown and Large Forest Fires: Dr. Phys.- Math. Sci. Diss. Tomsk, 2010. 282 p.
13. Poddubnyy A. Calculation of the Economic Effect from the Automation System Implementation. Antegra Consulting. Режим доступа: http://www.antegra.ru/news/experts/_det-experts/4/ (дата обращения: 06.11.19).
14. Porfiriev B.N. Fire by Order. Ekspert [Expert], no. 34(1130). Режим доступа: https://expert.ru/expert/2019/34/pozhar-po-prikazu/ (дата обращения: 06.11.19).
15. Stankevich T.S. Forest Fires. Certificate of State Registration of the Database No. RU2019620918, 2019.
16. Stankevich T.S. The Use of Convolutional Neural Networks to Forecast the Dynamics of Spreading Forest Fires in Real Time. Biznes-informatika [Business Informatics], 2018, vol. 4(46), pp. 17–27. DOI: 10.17323/1998-0663.2018.4.17.27
17. Adab H., Kanniah K.D., Solaimani K. Modeling Forest Fire Risk in the Northeast of Iran Using Remote Sensing and GIS Techniques. Natural Hazards, 2013, vol. 65, pp. 1723–1743. DOI: 10.1007/s11069-012-0450-8
18. Agarwal P.K., Patil P.K., Mehal R. A Methodology for Ranking Road Safety Hazardous Locations Using Analytical Hierarchy Process. Procedia – Social and Behavioral Sciences, 2013, vol. 104, pp. 1030–1037. DOI: 10.1016/j.sbspro.2013.11.198
19. Angayarkkani K., Radhakrishnan N. An Effective Technique to Detect Forest Fire Region through ANFIS with Spatial Data. 3rd International Conference on Electronics Computer Technology, Kanyakumari, April 8–10, 2011. Kanyakumari, India, IEEE, 2011, pp. 24–30. DOI: 10.1109/ICECTECH.2011.5941794
20. Byram G.M. Combustion of Forest Fuels. Forest Fire: Control and Use. Ed. by K.P. Davis. New York, McGraw-Hill, 1959, pp. 61–89.
21. Chuvieco E., Aguadoa I., Yebraa M., Nieto H., Salas J., Martín M.P. et al. Development of a Framework for Fire Risk Assessment Using Remote Sensing and Geographic Information System Technologies. Ecological Modelling, 2010, vol. 221, iss. 1, pp. 46–58.
22. Cruz M.G., Alexander M.E., Wakimoto R.H. Development and Testing of Models for Predicting Crown Fire Rate of Spread in Conifer Forest Stands. Canadian Journal of Forest Research, 2005, vol. 35, no. 7, pp. 1626–1639. DOI: 10.1139/x05-085
23. Davis R., Yang Z., Yost A., Belongie C., Cohen W. The Normal Fire Environment – Modeling Environmental Suitability for Large Forest Wildfires Using Past, Present, and Future Climate Normals. Forest Ecology and Management, 2017, vol. 390, pp. 173–186. DOI: 10.1016/j.foreco.2017.01.027
24. Dimopoulou M., Giannikos I. Spatial Optimization of Resources Deployment for Forest Fire Management. International Transactions in Operational Research, 2001, vol. 8, iss, 5, pp. 523–534. DOI: 10.1111/1475-3995.00330
25. Dimopoulou M., Giannikos I. Towards an Integrated Framework for Forest Fire Control. European Journal of Operational Research, 2002, vol. 152, iss. 2, pp. 476–486. DOI: 10.1016/S0377-2217(03)00038-9
26. European Forest Fire Information System (EFFIS). Available at: http://effis.jrc.ec.europa.eu (accessed 06.11.19).
27. FARSITE. Fire Area Simulator. Available at: https://www.firelab.org/project/farsite (accessed 06.11.19).
28. Finney M.A. FARSITE: Fire area simulator – Model Development and Evaluation. Research Paper RMRS-RP-4. Ogden, UT, Rocky Mountain Research Station, 1998. 47 p. DOI: 10.2737/RMRS-RP-4
29. FlamMap. Fire Analysis Desktop Application. Available at: https://www.firelab.org/project/flammap (accessed 06.11.19).
30. Forest Fires Data. National Forestry Database. Available at: http://nfdp.ccfm.org/en/data/fires.php#tab311 (accessed 06.11.19).
31. Gigovíc L., Pourghasemi H.R., Drobnjak S., Bai S. Testing a New Ensemble Model Based on SVM and Random Forest in Forest Fire Susceptibility Assessment and Its Mapping in Serbia’s Tara National Park. Forests, 2019, vol. 10, iss. 5, art. 408. DOI: 10.3390/f10050408
32. Guo F., Selvaraj S., Lin F., Wang G., Wang W., Su Z., Liu A. Geospatial Information on Geographical and Human Factors Improved Anthropogenic Fire Occurrence Modeling in the Chinese Boreal Forest. Canadian Journal of Forest Research, 2016, vol. 46, no. 4, pp. 582–594. DOI: 10.1139/cjfr-2015-0373
33. Land Cover Map ESA/CCI. Available at: http://maps.elie.ucl.ac.be/CCI/viewer/ (accessed 06.11.19).
34. Maeda E.E., Formaggio A.R., Shimabukuro Y.E., Arcoverde G.F.B, Hansen M.C. Predicting Forest Fire in the Brazilian Amazon Using MODIS Imagery and Artificial Neural Networks. International Journal of Applied Earth Observation and Geoinformation, 2009, vol. 11, iss. 4, pp. 265–272. DOI: 10.1016/j.jag.2009.03.003
35. Martínez J., Vega-García C., Chuvieco E. Human-Caused Wildfire Risk Rating for Prevention Planning in Spain. Journal of Environmental Management, 2009, vol. 90(2), pp. 1241–1252. DOI: 10.1016/j.jenvman.2008.07.005
36. Mavsar R., Cabán A.G., Varela E. The State of Development of Fire Management Decision Support Systems in America and Europe. Forest Policy and Economics, 2013, vol. 29, pp. 45–55. DOI: 10.1016/j.forpol.2012.11.009
37. NASA’s Fire Information for Resource Management System (FIRMS). Available at: https://firms.modaps.eosdis.nasa.gov/map/#z:3.0;c:44.286,17.596 (accessed 06.11.19).
38. Perminov V., Goudov A. Mathematical Modeling of Forest Fires Initiation, Spread and Impact on Environment. International Journal of GEOMATE, 2017, vol. 13, iss. 35, pp. 93–99. DOI: 10.21660/2017.35.6704
39. Prometheus. Canadian Wildland Fire Growth Simulation Model. Available at: http://www.firegrowthmodel.ca/prometheus/overview_e.php (accessed 06.11.19).
40. Rodríguez y Silva F., Martínez J.R.M., Machuca M.Á.H., Leal J.M.R. VISUALSEVEIF, a Tool for Integrating the Behavior Simulation and Economic Evaluation of the Impacts of Wildfires. Proceedings of the Fourth International Symposium on Fire Economics, Planning, and Policy: Climate Change and Wildfires. General Technical Report 245. Albany, CA, USDA, 2013, pp. 163–178. DOI: 10.2737/PSW-GTR-245
41. Rothermel R.C. Predicting Behavior and Size of Crown Fires in the Northern Rocky Mountains. Research Paper INT-438. Ogden, UT, Intermountain Forest Experiment Station, 1991. 46 p. DOI: 10.2737/INT-RP-438
42. Safi Y., Bouroumi A. Prediction of Forest Fires Using Artificial Neural Networks. Applied Mathematical Sciences, 2013, vol. 7, no. 5-8, pp. 271–286. DOI: 10.12988/ams.2013.13025
43. Satir O., Berberoglu S., Donmez C. Mapping Regional Forest Fire Probability Using Artificial Neural Network Model in a Mediterranean Forest Ecosystem. Geomatics, Natural Hazards and Risk, 2016, vol. 7, iss. 5, pp. 1645–1658. DOI: 10.1080/19475705.2015.1084541
44. Sánchez J. Los incendios forestales y las prioridades de investigaciónen México. Tomo II. México, Congreso Forestal Mexicano, 1989, pp. 719–723.
45. US Wildfires. NOAA’s National Centers for Environmental Information (NCEI). Available at: https://www.ncdc.noaa.gov (accessed 06.11.19).
46. Van Wagner C.E. Conditions for the Start and Spread of Crown Fire. Canadian Journal of Forest Research, 1977, vol. 7, no. 1, pp. 23–34. DOI: 10.1139/x77-004
47. Ventusky Aplication. InMeteo. Available at: https://www.ventusky.com (accessed 06.11.19).
48. Wildfire Analyst Software. Available at: https://www.wildfireanalyst.com/ (accessed 06.11.19).