Geodesic
Information processing for the decision support system for fire monitoring of forest areas
Izvestiya of Saratov University. Mathematics. Mechanics. Informatics, Tome 23 (2023) no. 1, pp. 126-138 
A. A. Sorokin; N. S. Maltseva; D. V. Kutuzov; A. V. Osovsky. Information processing for the decision support system for fire monitoring of forest areas. Izvestiya of Saratov University. Mathematics. Mechanics. Informatics, Tome 23 (2023) no. 1, pp. 126-138. http://geodesic.mathdoc.fr/item/ISU_2023_23_1_a10/
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     title = {Information processing for the decision support system for~fire~monitoring of forest areas},
     journal = {Izvestiya of Saratov University. Mathematics. Mechanics. Informatics},
     pages = {126--138},
     year = {2023},
     volume = {23},
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     url = {http://geodesic.mathdoc.fr/item/ISU_2023_23_1_a10/}
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Voir la notice de l'article provenant de la source Math-Net.Ru

The purpose of our study was to formulate provisions for obtaining an integral assessment characterizing the rating of forest areas in terms of fire hazard. We obtained this estimate based on the aggregation of many parameters characterizing climatic conditions and factors that take into account anthropogenic influence in a given area of the forest. Considering the heterogeneity of such parameters, we used the methods of fuzzy inference and the theory of fuzzy sets to aggregate them. The complex for determining the assessment of the forest area is implemented in the form of a hierarchical fuzzy inference system. We investigated the process of functioning of the formed complex and found that its output pattern is predominantly stepwise. This result makes it possible to classify the analyzed forest areas into states groups. Further studies of the classes of states formed by us by the methods of cluster analysis make it possible to identify areas with similar characteristics. The use of the classification results makes it possible to rank forest areas according to the order of preventive or preparatory measures to reduce fire hazard or increase responsiveness in case of a fire. The results obtained by us are aimed at using in decision support systems for the management of forests and other types of adjacent territories.

[1] Kukhar I. V., Berdnikova L. N., Orlovskii S. N., Martynovskaia S. N., Korshun V. N., Karnaukhov A. I., “The impact of harmful and dangerous factors of forest fires on the environment”, Conifers of the Boreal Area, 37:5 (2019), 307–312 (in Russian)

[2] Sokolov M. M., “Russia's strategies for the introduction of cross-border carbon regulation in the EU”, Geoeconomics of Energy, 15:3 (2021), 84–97 (in Russian) | DOI

[3] Minakov E. I., Kalistratov D. S., Mirchuk S. G., “The model of information-measuring system of forest fires videomonitoring”, Izvestiya Tula State University. Technical Science, 2017, no. 1, 1–2 (in Russian)

[4] Gubenko I. M., Rubinshtein K. G., “Comparative analysis of methods of fire danger indexes evaluation”, Proceedings of the Hydrometeorological Research Center of the Russian Federation, 2012, no. 347, 207–222 (in Russian)

[5] Wotton M. B., “Interpreting and using outputs from the Canadian Forest Fire Danger Rating System in research applications”, Environmental and Ecological Statistics, 16:2 (2009), 107–131 | DOI | MR

[6] Sharples J. J., McRae R. H. D., Weber R. O., Gill A. M., “A simple index for assessing fire danger rating”, Environmental Modelling Software, 24:6 (2009), 764–774 | DOI

[7] Van Wagner C. E., Development and structure of the Canadian Forest Fire Weather Index System, Forestry Technical Report, No 35, Canadian Forestry Service, Headquarters, Ottawa, 1987, 35 pp. (accessed 20 March 2022) https://cfs.nrcan.gc.ca/publications?id=19927

[8] Galushkin A. I., Neural Networks: Fundamentals of Theory, Goryachaya Liniya-Telekom, M., 2010, 496 pp. (in Russian)

[9] Pegat A., Fuzzy Modeling and Control, BINOM. Laboratoriya znaniy, M., 2017, 800 pp. (in Russian)

[10] Shtovba S. D., Designing fuzzy systems by means of MATLAB, Goryachaya Liniya-Telekom, M., 2007, 288 pp. (in Russian)

[11] Lee K. H., First Course on Fuzzy Theory and Applications, Springer, Berlin–Heidelberg, 2005, 335 pp. | DOI

[12] Sorokin A. A., “Improvement of information-analytical complexes based on hierarchical systems of fuzzy output”, Large-Scale Systems Control, 88 (2020), 99–123 (in Russian) | DOI

[13] Yager R. R., Filev D. P., “Approximate clustering via the mountain method”, IEEE Transactions on Systems, Man, and Cybernetics, 24:8 (1994), 1279–1284 | DOI

[14] Leont'ev V. K., “On measures of similarity and distances between objects”, Computational Mathematics and Mathematical Physics, 49:11 (2009), 1949–1965 | DOI | MR