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Mathematical Modeling of Human Activity on Forested Areas from Point Objects of Railway Infrastructure in a Two-Dimensional Statement

Nikolay Viktorovich Baranovskiy(1*), Anna Vladimirovna Ignateva(2)

(1) National Research Tomsk Polytechnic University, Russian Federation
(2) National Research Tomsk State University, Russian Federation
(*) Corresponding author


DOI: https://doi.org/10.15866/irea.v10i1.21196

Abstract


Human activity causes a large quantity of wildfires in areas close to the location of different industrial or transport infrastructure. All the infrastructure facilities of JSC Russian Railways may be classified into linear, point and area sources of human activity. This work purpose is to suggest an approach for predicting, assessing, and monitoring the human activity on forested areas based on a deterministic mathematical model. Partial differential equations have been solved using the finite difference method. The program realization is executed in the RAD Studio software. Expressions like the heat conduction equation are used to describe the propagation of human activity around railway facilities. Different railway objects have produced different distribution of the virtual (possible) number of forest fires (VNF) caused by the different level of human activity. Different sizes of the railway facilities have also showed that a highest VNF is predicted for large one. The proposed mathematical model is applicable to predict wildfires due to human activity. This paper discusses the possibility of predicting forest fire dangers near point infrastructure facilities of JSC Russian Railways. The results obtained are part of a large project aimed to develop methods for predicting, assessing, and monitoring forest fire danger near infrastructure facilities of Russian Railways.
Copyright © 2022 The Authors - Published by Praise Worthy Prize under the CC BY-NC-ND license.

Keywords


Human Activity; Mathematical Model; Point Source; Virtual Number of Forest Fires; Forest Fire Danger; Railway

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References


Ruffault J., Mouillot F.(2017). Contribution of human and biophysical factors to the spatial distribution of forest fire ignitions and large wildfires in a French Mediterranean region. International Journal of Wildland Fire, 26, 498-508.
https://doi.org/10.1071/WF16181

Pew K. L., Larsen C. P. S. GIS analysis of spatial and temporal patterns of hu¬man-caused wildfires in the temperate rain forest of Vancouver Island, Canada // Forest Ecology and Management. 2001. V. 140, N 1. P. 1-18.
https://doi.org/10.1016/S0378-1127(00)00271-1

Komarov K.L., Kachinsky V.A. Modeling of emergency response processes in railway transport // Transport of the Urals. 2011. No. 2 (29). P. 25-30. (In Russian)

Ilyavin M.V., Katin V.D. On the problem of ensuring fire safety during the reform of the Transsib // In the collection: Transsib: at the forefront of reforms. materials of the international scientific and practical conference. Irkutsk State University of Railways; Transbaikal Institute of Railway Transport. 2016. P. 254-259. (In Russian)

Baranovskiy N.V. Testing the system of assimilation of data on the level of human activity for a linear source of human activity / Environmental safety management system: a collection of proceedings of the correspondence international scientific and practical conference. Yekaterinburg, Russian Federation: USTU - UPI. 2007. P. 319 - 322. (In Russian).

Baranovskiy N., Kritski O. Probabilistic simulation of non-uniform human activity in the context of forest fires // International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management, SGEM, 2019, 19 (2.1), pp. 525-530.
https://doi.org/10.5593/sgem2019/2.1/S07.069

Baranovskiy N.V. Mathematical simulation of human activity from a linear source using partial differential equations in the context of the forest fires occurrence // Advances in Differential Equations and Control Processes. 2018. Vol. 19. P. 237-262.
https://doi.org/10.17654/DE019030237

Samarskii A.A., Vabishchevich P.N. Computational Heat Transfer, Vol. 1. Mathematical Modelling, Wiley, Chichester, 1995, 418 p.

Samarskii A.A., Vabishchevich P.N. Computational Heat Transfer, Vol. 2. The Finite Difference Method, Wiley, Chichester, 1995, 432 p.

Baranovskiy N.V. (Ed.). (2021). Forest Fire Danger Prediction Using Deterministic-Probabilistic Approach. IGI Global.
https://doi.org/10.4018/978-1-7998-7250-4

Baranovskiy N.V. (2020). Predicting Forest Fire Numbers Using Deterministic-Probabilistic Approach. In Baranovskiy, N. V. (Eds.), Predicting, Monitoring, and Assessing Forest Fire Dangers and Risks (pp. 89-100). IGI Global.
https://doi.org/10.4018/978-1-7998-1867-0.ch004

Grishin A.M. (1997) Mathematical modeling of forest fire and new methods of fighting them. Publishing House of the Tomsk State University: Tomsk, Russian Federation.

Grishin A.M., Filkov A.I. (2011) A deterministic-probabilistic system for predicting forest fire danger. Fire Safety Journal, Vol. 46, pp. 56-62.
https://doi.org/10.1016/j.firesaf.2010.09.002

Al Janabi S., Al Shourbaji I., Salman M.A. (2017) Assessing the suitability of soft computing approaches for forest fires prediction. Applied Computing and Informatics.
https://doi.org/10.1016/j.aci.2017.09.006

Read N., Duff T.J., Taylor P.G. (2018) A lightning-induced wildfire ignition predicting model for operational use. Agricultural and Forest Meteorology, Vol. 253-254, pp. 233-246.
https://doi.org/10.1016/j.agrformet.2018.01.037

Canadian Wildland Fire Information System. Official site. (Accessed 03 March 2021).
Access: http://cwfis.cfs.nrcan.gc.ca/home

Wotton B.M. (2009) Interpreting and using outputs from the Canadian Forest Fire Danger Rating System in research applications. Environmental and Ecological Statistics, Vol. 16, pp. 107-131.
https://doi.org/10.1007/s10651-007-0084-2

Gould J.S., Patriquin M.N., Wang S., McFarlane B.L., Wotton B.M. (2013) Economic evaluation of research to improve the Canadian forest fire danger rating system. Forestry, Vol. 86, pp. 317-329.
https://doi.org/10.1093/forestry/cps082

Lee B.S., Alexander M.E., Hawkes B.C., Lynham T.J., Stocks B.J., Englefield P. (2002) Information systems in support of wildland fire management decision-making in Canada. Computers and Electronics in Agriculture, Vol. 37, pp. 185-198. doi: 10.1016/S0168-1699(02)00120-5
https://doi.org/10.1016/S0168-1699(02)00120-5

Martell D.L. (2000) A Markov Chain Model of Day to Day Changes in the Canadian Forest Fire Weather Index. International Journal of Wildland Fire, Vol. 9, pp. 265-273
https://doi.org/10.1071/WF00020

WFAS Wildland Fire Assessment System. Official site. (Accessed 03 March 2021).
Access: http://www.wfas.net

Deeming J.E., Lancaster J.W., Fosberg M.A., Furman W.R., Schroeder M.J. (1974) The National Fire Danger Rating System. United States Department of Agriculture, Forest Service, Research Paper RM-84, Rocky Mountain Forest and Range Experiment Station, Fort Collins, Colorado. 165 P.
https://doi.org/10.5962/bhl.title.98707

Deeming J.E., Burgan R.E., Cohen J.D. (1977) The National Fire Danger Rating System - 1978. United States Department of Agriculture, Forest Service, General Technical Report INT-39, Intermountain Forest and Range Experiment Station, Odgen, Utah. 66 P.

European Forest Fire Information System. Official site. (Accessed 03 March 2021).
Access: http://effis.jrc.ec.europa.eu

Viegas D.X., Bovio G., Ferreira A., Nosenzo A., Sol, B. (2000) Comparative study of various methods of fire danger evaluation in Southern Europe. International Journal of Wildland Fire, Vol. 10, pp. 235-246.
https://doi.org/10.1071/WF00015

Information system for remote monitoring of forest fires ISDM-Rosleskhoz. Official site. (Accessed 03 March 2021).
Access: https://nffc.aviales.ru/main_pages/index.shtml

Podolskaya A.S., Ershov D.V., Shulyak P.P. (2011) Application of the method for assessing the probability of occurrence of forest fires in ISDM-Rosleskhoz. Modern problems of remote sensing of the Earth from space, Vol. 8, pp. 118 - 126. (In Russian).

Baranovskiy N.V. (2015) Project of the eurasian segment of the new system of the forest fire risk prediction based on information and computer technologies. Journal of Automation and Information Sciences, Vol. 47, pp. 40-56.
https://doi.org/10.1615/JAutomatInfScien.v47.i3.40

Baranovskiy, N., Forest Fire Danger Assessment Using SPMD-Model of Computation for Massive Parallel System, (2017) International Review on Modelling and Simulations (IREMOS), 10 (3), pp. 193-201.
https://doi.org/10.15866/iremos.v10i3.10570

Baranovskiy N., Zharikova M. (2014) A web-oriented geoinformation system application for forest fire danger in the typical forests of the Ukraine. Lecture Notes in Geoinformation and Cartography, article 199669, pp. 13-22.
https://doi.org/10.1007/978-3-319-08180-9_2

Melekhov I.S. (1947) Nature of forest and forest fires. Nauka: Arkhangelsk, USSR (In Russian)

Andreev Y.A., Larchenko G.F. (1987) Socio-psychological aspects of recreational forest visits and the emergence of fires. Forest fires and the fight against them. VNIILM: Moscow, USSR. (In Russian).

Kurbatskiy N.P. (1964) The problem of forest fires. The emergence of forest fires. Nauka: Moscow, USSR. (In Russian).

Melluma A.Zh., Rungule R.H., Emsis I.V. (1982) Recreation on nature as a nature protection problem. Zinatne: Riga, USSR. (In Russian).

Telitsin G.P. (1984) Study of the connection between forest attendance and the occurrence of fire. Lesovedenie. pp. 59-63. (In Russian).

Cardille J.A., Ventura S.J., Turner M.G. (2001) Environmental and Social Factors of Influencing Wildfires in the Upper Midwest, United States. Ecological Applications, Vol. 11, pp. 111-127.
https://doi.org/10.1890/1051-0761(2001)011[0111:EASFIW]2.0.CO;2

Ganteaume A., Guerra F. (2018) Explaining the spatio-seasonal variation of fires by their causes: The case of southeastern France. Applied Geography, Vol. 90, pp. 69-81.
https://doi.org/10.1016/j.apgeog.2017.11.012

Ye J., Wu M., Deng Z., Xu S., Zhou R., Clarke K.C. (2017) Modeling the spatial patterns of human wildfire ignition in Yunnan province, China. Applied Geography, Vol. 89, pp. 150-162.
https://doi.org/10.1016/j.apgeog.2017.09.012

Diez-Delgado R., Lloret F., Pons X. (2004) Statistical analysis of fire-frequency models for Catalonia (NE Spain, 1975-1998) based on fire scar maps from Landsat MSS data. International Journal of Wildland Fire, Vol. 13, pp. 89-99.
https://doi.org/10.1071/WF02051

Hong H., Tsangaratos P., Ilia I., Liu J., Zhu A.-X., Xu C. (2018) 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 Country, China. Science of the Total Environment, Vol. 630. pp. 1044 - 1056.
https://doi.org/10.1016/j.scitotenv.2018.02.278

Haupt L.R., Haupt S.E. (2004) Practical genetic algorithms, 2nd edition. John Wiley & Sons Inc.
https://doi.org/10.1002/0471671746

Chen W., Xie X., Wang J., Pradhan B., Hong H., Tien Bui D., Duan Z., Ma J. (2017) A comparative study of logistic model tree, random forest and classification and regression tree models for spatial prediction of landslide susceptibility. Catena, Vol. 151, pp. 147 - 160.
https://doi.org/10.1016/j.catena.2016.11.032

Dormann C.F., Elith J., Bacher S., Bushmann C., Carl G., Carre G., Marquez J.R.G., Gruber B., Lafourcade B., Leitao P.J., Munkemuller T., McClean C., Osborne P.E., Reineking B., Schroder B., Skidmore A.K., Zurell D., Lautenbach S. (2013) Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography, Vol. 36, pp. 27 - 46.
https://doi.org/10.1111/j.1600-0587.2012.07348.x

Ghimire B., Rogan J., Galiano V.R., Panday P., Neeti N. (2012) An evaluation of bagging, boosting and random forests for land-cover classification in CapeCod, Massachusetts, USA. GIScience and Remote Sensing, Vol. 49, pp. 623 - 643.
https://doi.org/10.2747/1548-1603.49.5.623

Duan K., Keerthi S., Poo A. (2001) Evaluation of simple performance measures for tuning SVM hyperparameters (Technical report). National University of Singapore. Department of Mechanical Engineering, Singapore.

Grala K., Grala R.K., Hussain A., Cooke W.H. (2017) Impact of human factors on wildfire occurrence in Mississippi, USA. Forest Policy and Economics, Vol. 81, pp. 38 - 47.
https://doi.org/10.1016/j.forpol.2017.04.011

Jimenez-Ruano A., Rodrigues Mimbrero M., de la Riva Fernandez J. (2017) Understanding wildfires in mainland Spain. A comprehensive analysis of fire regime features in a climate-human context. Applied Geography, Vol. 89, pp. 100 - 111.
https://doi.org/10.1016/j.apgeog.2017.10.007

Gonzalez-Hidalgo J.C., Pena-Angulo D., Brunetti M., Cortesi N. (2015) MOTEDAS: A new monthly temperature database for mainland Spain and the trends in temperature (1951 - 2010). International Journal of Climatology, Vol. 35, pp. 4444 - 4463.
https://doi.org/10.1002/joc.4298

Flury B.N. (1984) Common principal components in K groups. Journal of the American Statistical Association, Vol. 79, pp. 892 - 898.
https://doi.org/10.2307/2288721

Kaiser H.F. (1958) The varimax criterion for analytic rotation in factor analysis. Psychometrika, Vol. 28, pp. 187 - 200.
https://doi.org/10.1007/BF02289233

Hu T., Zhou G. (2014) Drivers of lightning- and human-caused fire regimes in the Great Xing'an Mountains. Forest Ecology and Management, Vol. 329, pp. 49 - 58.
https://doi.org/10.1016/j.foreco.2014.05.047

Liu Q., Gao P., Wang Z. (2012) Great Xing'an Mountains: the relationship between population growth and economic development. Statist Consult, Vol. 6, pp. 17 - 19.

Liu Z.H., Yang J., Chang Y., Weisberg P.J., He H.S. (2012) Spatial patterns and drivers of fire occurrence and its future trend under climate change in a boreal forest of Northeast China. Global Change Biology, Vol. 18, pp. 2041 - 2056.
https://doi.org/10.1111/j.1365-2486.2012.02649.x

Curt T., Frejaville T., Lahaye S. (2016) Modelling the spatial patterns of ignition causes and fire regime features in southern France: Implications for the prevention policy. International Journal of Wildland Fire, Vol. 25, pp. 785 - 796.
https://doi.org/10.1071/WF15205

Fusco E.J., Abatzoglou J.T., Balch J.K., Finn J.T., Bradley B.A. (2016) Quantifying the human influence on fire ignition across the western USA. Ecological Applications, Vol. 26, pp. 2390 - 4201.
https://doi.org/10.1002/eap.1395

Zhang Y., Lim S., Sharples J.J. (2016) Modelling spatial patterns of wildfire occurrence in South-Eastern Australia. Geomatics, Natural Dangers and Risk, Vol. 7, pp. 1800 - 1815.
https://doi.org/10.1080/19475705.2016.1155501

Agterberg F.P., Cheng Q. (2002) Conditional independence test for weight-of-evidence modeling. Natural Resources Research, Vol. 11, pp. 249 - 255.
https://doi.org/10.1023/A:1021193827501

Imetkhenov A.B. Forest fires of Buryatia: analysis of the current state and some recommendations for preventive maintenance // Coll. mat-lov scientific-practical. conf. - Ulan-Ude, 2016. P. 68-73. (In Russian).

Borisova T.A. Forest fires in Buryatia: causes and effects // Vestnik VSU, series: geography, geoecology, 2017. No. 2. P. 78-84. (In Russian).

Chebotaeva D.O., Alymbaeva Zh.B. Geoecological aspects of forest fires in the Republic of Buryatia // Proceedings of the V International scientific conference dedicated to the 130th anniversary of the Herbarium named after P.N. Krylov and the 135th anniversary of the Siberian Botanical Garden of Tomsk State University "Problems of studying the vegetation cover of Siberia." Tomsk. Publishing house: TSU. 2015. P. 150-153. (In Russian).

Sidorov A.A., Khankhunov Yu.M. On problems with forest fires in the Republic of Buryatia // Coll. mat-lov scientific-practical. conf. with int. participation. - Ulan-Ude, 2015.P. 84-89. (In Russian).

Sofronova T.M., Volokitina A.V., Sofronov M.A. Improving the assessment of fire danger by weather conditions in the mountain forests of the Southern Baikal region. Krasnoyarsk, 2007. 236 p. (In Russian).

Belyakin A.A., Volokitina A.V. Pyrological characteristics of forest types in the southern Baikal region // Bulletin of KrasGAU. Ecology. 2010. No. 7. P. 91-96. (In Russian).

Evdokimenko M.D. Geography and causes of fires in the Baikal forests // News of higher educational institutions. Forest Journal. 2013. N 4.P. 30 - 39. (In Russian).

Evdokimenko M. D. On long-term predicting of high fire danger of forests in the Baikal region // Forestry. 2000. No. 1. P. 47-50. (In Russian).

Ukraintsev A. V., Plyusnin A. M. Forest fires in the Zaigraevsky district of the Republic of Buryatia in 2010-2012: causes of fire and damage // Geography and natural resources. 2015. No. 2. P. 60-65. (In Russian).

Makarenko EL Forest fires and their consequences in the Central ecological zone of the Baikal natural territory // Interactive science, 2016. No. 5. P. 9-12. (In Russian).
https://doi.org/10.21661/r-111897

Imetkhenov A.B. Forest fires in Buryatia: analysis of the current state and some recommendations for preventive maintenance // Materials of the VIII All-Russian scientific-practical conference "Actual issues of technosphere safety". Ulan-Ude. 2015. P. 75-79. (In Russian).

Dorzhiev Ts. Z., Yuukhai Bao, Badmaeva E. N., Batsaykhan V., Urbazaev Ch. B., Yushan Forest fires in the Republic of Buryatia in 2002-2016. // The Nature of Inner Asia, No. 3 (4), 2017, pp. 22-37. (In Russian).

Territorial body of the Federal State Statistics Service for the Republic of Buryatia. (Accessed 04 April 2021).
Access mode: https://burstat.gks.ru/transport_communications

Resolution of the Government of the Republic of Buryatia "On the strategy of socio-economic development of the Republic of Buryatia until 2025" dated December 15, 2007 No. 410. (Accessed 04 April 2021).
Access mode: http://docs.cntd.ru/document/446260821

Aslamova V.S., Frolova E.Yu. System analysis of the causes of fires on locomotives of JSC "Russian Railways" // Modern technologies. System analysis. Modeling, 2018, vol. 60, no. 4. P. 63-70. (In Russian).
https://doi.org/10.26731/1813-9108.2018.4(60).63-70

Samarskii A.A. The theory of difference schemes. New York - Basel. Marcel Dekker, Inc, 2001, 761 P.
https://doi.org/10.1201/9780203908518

Samarskii A.A., Vabishchevich P.N. Computational Heat Transfer, Vol. 1. Mathematical Modelling, Chichester: Wiley. 1995.

Samarskii A.A., Vabishchevich P.N. Computational Heat Transfer, Vol. 2. The Finite Difference Method, Chichester: Wiley. 1995.

Baranovskiy, N., Kirienko, V., Mathematical Simulation of Forest Fuel Element at the Crown Forest Fire Impact Taking Into Account Multiphase Reactive Media Mechanics Fundamentals, (2020) International Review of Mechanical Engineering (IREME), 14 (8), pp. 504-515.
https://doi.org/10.15866/ireme.v14i8.19655

JSC Russian Railways. (Accessed April 02, 2021).
Access mode: http://rzd-company.ru/

Origin Lab Official (Accessed: 2019-05-25).
Web-site: https://www.originlab.com/

RAD Studio Accessed: (2019-05-25).
https://www.embarcadero.com/ru/products/rad-studio

Jarratano J., Riley G. Expert systems: design principles and programming. Translation from English. Moscow: Williams Publishing House. 2007. 1152 p. (In Russian).

Russell S., Norvig P. Artificial intelligence: a modern approach. Translation from English. Moscow: Williams Publishing House. 2007. 1408 P. (In Russian).

Trifonova T.A., Mishchenko N.V., Krasnoshchekov A.N. Geographic information systems and remote sensing in environmental research. Moscow: Academic Project, 2005. 352 p. (In Russian).

Garliz I.V. et al. Geoinformation technologies: Principles, international experience, development prospects. Moscow: 1989. (In Russian).

Shaytura S.V. Geographic information systems and methods of their creation. Kaluga: N. Bochkareva Publishing House. 1998. 252 p. (In Russian).

Baranovskiy N.V. Mathematical modeling of the most probable scenarios and conditions for the occurrence of forest fires. PhD Thesis. Tomsk: Tomsk State University. 2007. 153 P. (In Russian).

Kunkel S., Teumer T., Dörnhofer P., Schlachter K., Weldeslasie Y., Kühr M., Rädle M. , Repke J.-U. Determination of heat transfer coefficients in direct contact latent heat storage systems // Applied Thermal Engineering 145 (2018) 71-79.
https://doi.org/10.1016/j.applthermaleng.2018.09.015

Li F., Bai B. A model of heat transfer coefficient for supercritical water considering the effect of heat transfer deterioration // International Journal of Heat and Mass Transfer. 2019. Vol. 133, P. 316-329.
https://doi.org/10.1016/j.ijheatmasstransfer.2018.12.121

Larjavaara M., Kuuluvainen T., Rita H. Spatial distribution of lightning-ignited fires in Finland // Forest Ecology and Management. 2005. V. 208, N 1-3. P. 177-188.
https://doi.org/10.1016/j.foreco.2004.12.005


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