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Assessment, Monitoring and Prediction of Forest Fire Danger Using Atmospheric Soil Measuring Complex

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Large-scale combustion of forest fuels stops when the entire forest area is burned out, when a natural obstacle is encountered on the path of a forest fire front (channel of a large river, mountain range) or at the beginning of the rainy season. In this situation, the problem can be solved only in anticipation of a catastrophic phenomenon, which is possible only when developing a methodology for assessing, monitoring, and predicting forest fire dangers. The purpose of this article is to develop a methodology for assessing, monitoring, and predicting forest fire dangers using an atmospheric-soil measuring complex, which provides information on the meteorological parameters and some data on soil in the control zone of a forest fire situation. The structure of the instrumental network for monitoring environmental parameters in order to forestall forest fires is proposed. Criteria for assessing forest fire dangers have been developed using data from the atmospheric-soil measuring complex located at one of the stations in the Republic of Buryatia. Typical results of assessment, monitoring, and predicting forest fire danger using these data are presented. Recommendations are proposed for the implementation of the methodology under consideration in the practice of forest fire protection.
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Forest Fire Danger; Assessment; Monitoring; Predicting; Meteorological Conditions; Atmospheric Soil Measuring Complex

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Baranovskiy N.V., Kuznetsov G.V. Forest fire occurrences and ecological impact prediction: monograph. Publishing House of the Siberian Branch of the Russian Academy of Science: Novosibirsk, Russian Federation, 2017;

Baranovskiy N.V. Thermophysical aspects of predictive modeling of forest fire danger. Dr. Sc. Dissertation. Tomsk: Tomsk Polytechnic University. 2012. 436 p. (In Russian)

Brushlinsky N.N, Ahrens M, Sokolov S.V, Wagner P. World fire statistics. International association of fire and rescue services. 2019. No. 24. 68 P.

Baranovskiy N. V. (2020). Predicting, Monitoring, and Assessing Forest Fire Dangers and Risks. IGI Global.

Baranovskiy N.V. Mathematical Simulation of Anthropogenic Load on Forested Territories for Point Source. In N. Baranovskiy (Ed.), Predicting, Monitoring, and Assessing Forest Fire Dangers and Risks. Hershey, PA: IGI Global. 2020. (pp. 64-88)

Badmaev N.B., Bazarov A.V., Sychev R.S. (2020). Forest Fire Danger Assessment Using Meteorological Trends: Case Study. In Baranovskiy, N. V. (Ed.), Predicting, Monitoring, and Assessing Forest Fire Dangers and Risks (pp. 183-208). IGI Global. http://doi:10.4018/978-1-7998-1867-0.ch008

Baranovskiy N.V., Bazarov A.V. Technologies of physical monitoring and mathematical modeling for estimation of ground forest fuel fire condition (2016) EPJ Web of Conferences, 110, 6 P.

Baranovskiy N., Bazarov A. Methods of using ASMC data to assess forest fire danger (2019) International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management, SGEM, 19 (5.1), pp. 453-459.

Zadeh T.A., Malzer M., Simon J., Beck S., Moll J., Krozer V. Range-dopler analysis for rain detection at Ka-Band: numerical and experimental results from laboratory and field measurements, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020. Vol. 13. P. 1027 – 1033.

Skolnik M. Radar Handbook. New York, USA: McGraw-Hill. 2008.

Gekat F., Meischner P., Friedrich K., Hagen M., Koistinen J., Michelson D.B., Huuskonen A. The state of weather radar operations, networks and products. In Weather Radar: Principles and Advanced Applications, Ed. P. Meischner. Berlin, Germany: Springer. 2004. Pp. 1 – 51.

Lei L., Zhang C., Doviak R.J., Karimkashi S. Comparison of theoretical biases in estimating polarimetric properties of precipitation with weather radar using parabolic reflector, or planar and cylindrical arrays, IEEE Transactions in Geoscience and Remote Sensing. 2015. Vol. 53. Pp. 4313 – 4327.

Chandra A., Zhang C., Kollias P., Matrosov S., Szyrmer W. Automated rain rate estimates using the Ka-band ARM zenith radar (KAZR), Atmospheric Measurement Techniques. 2015. Vol. 8. Pp. 3685 – 3699.

Gorsdorf U., Lehmann V., Bauer-Pfundstein M., Peters G., Vavriv D., Vinogradov V., Volkov V. A 35-GHz polarimetric Doppler radar for long-term observations of cloud parameters – description of system and data processing, Journal of Atmospheric and Oceanic Technology. 2015. Vol. 32. Pp. 675 – 690.

Moll J., Simon J., Malzer M., Krozer V., Pozdniakov D., Salman R., Durr M., Feulner M., Nuber A., Friedmann H. Radar imaging system for in-service wind turbine blades inspections: initial results from a field installation at a 2MW wind turbine, Progress in Electromagnetics Research. 2018. Vol. 162. Pp. 51 – 60.

Moll J., Arnold P., Malzer M., Krozer V., Pozdniakov D., Salman R., Rediske S., Scholz M., Friedmann H., Nuber A. Radar-based structural health monitoring of wind turbine blades: The case of damage detection, Structural Health Monitoring. 2018. Vol. 17. Pp. 815 – 822.

Boluwade A. Spatial-temporal assessment of satellite-based rainfall estimates in different precipitation regimes in water-scarce and data-sparse regions, Atmosphere. 2020. Vol. 11. Article 901.

Tropical Rainfall Measuring Mission. (Accessed 19 November 2020).

Integrated Multi-satellite Retrievals for GPM. (Accessed 19 November 2020)

Boluwade A., Stadnyk T., Fortin V., Roy G. Assimilation of precipitation Estimates from the Integrated Multisatellite Retrievals for GPM (IMERG, early Run) in the Canadian Precipitation Analysis (CaPA), Journal of Hydrology: Regional Studies. 2017. Vol. 14. Pp. 10 – 22.

Huffman G.J., Adler R.F., Bolvin D.T., Nelkin E.J. The TRMM Multi-satellite Precipitation Analysis (TMPA). Chapter 1, In Satellite Rainfall Applications for Surface Hydrology; Springer: Dordrecht, The Netherlands. 2010. pp. 3 – 22.

Horishima K. Rainfall observation from Tropical Rainfall Measuring Mission (TRMM) satellite, Journal of Visualization. 1999. Vol. 2. Pp. 93 – 98.

Flaming G.M. Measurement of global precipitation, In Proceedings of the 2004 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2004), Anchorage, AK, USA, 20–24 September 2004; IEEE: New York, NY, USA, 2004.

Kummerow C., Simpson J., Thiele O., Barnes W., Chang A., Stocker E., Adler R., Aimin Hou, Kakar R., Wentz F., Ashcroft P., Kozu T., Hong Y., Okamoto K., Iguchi T., Kuroiwa H., Im E., Haddad Z., Huffman G., Ferrier B., Olson W., Zipser E., Smith E.A., Wilheit T., North G., Krishnamurti T., Nakamura K. The Status of the Tropical Rainfall Measuring Mission (TRMM) after Two Years in Orbit, Journal of Applied Meteorology. 2000. Vol. 39. Pp. 1965 – 1982.<1965:tsottr>;2

Yuan F., Wang B., Shi C., Cui W., Zhao C., Liu Y., Ren L., Zhang L., Zhu Y., Chen T., Jiang S., Yang X. Evaluation of hydrological utility of IMERG Final run V05 and TMPA 3B42V7 satellite precipitation products in the Yellow River source region, China, Journal of Hydrology. 2018. Vol. 567. Pp. 696 – 711.

Silva Lelis L.C., Duarte Bosquilia R.W., Duarte S.N. Assessment of Precipitation Data Generated by GPM and TRMM Satellites, Revista Brasileira de Meteorologia. 2018. Vol. 33. Pp. 153 – 163.

Rozante J.R., Vila D.A., Chiquetto J.B., Fernandes A.A., Alvim D.S. Evaluation of TRMM/GPM Blended Daily Products over Brazil, Remote Sensing. 2018. Vol. 10. Article 882.

Xu R., Tian F.Q., Yang L., Hu H.C., Lu H., Hou A.Z. Ground validation of GPM IMERG and TRMM 3B42V7 rainfall products over southern Tibetan Plateau based on a high-density rain gauge network, Journal of Geophysical Research: Atmospheres. 2017. Vol. 122. Pp. 910 – 924.

Zhang S., Wang D., Qin Z., Zheng Y., Guo J. Assessment of the GPM and TRMM Precipitation Products Using the Rain Gauge Network over the Tibetan Plateau, Journal of Meteorological Research. 2018. Vol. 32. Article 324.

Fehlmann M., Rohrer M., von Lerber A., Stoffel M. Automated precipitation monitoring with the Thies disdrometer: biases and ways for improvement, Atmospheric Measurement Techniques. 2020. Vol. 13. Pp. 4683 – 4698.

Pickering B.S., Neely III R.R., Harrison D. The disdrometer verification network (DiVeN): a UK network of laser precipitation instruments, Atmospheric Measurement Techniques. 2019. Vol. 12. Pp. 5845 – 5861.

Angulo-Martínez M., Beguería S., Latorre B., Fernández Raga M. Comparison of precipitation measurements by OTT Parsivel2 and Thies LPM optical disdrometers, Hydrology and Earth System Science. 2018. Vol. 22. Pp. 2811 – 2837.

Frasson R.P.d.M., Krajewski W.F. Characterization of the drop-size distribution and velocity–diameter relation of the throughfall under the maize canopy, Agricultural and Forest Meteorology. 2011. Vol. 151. Pp. 1244 – 1251.

Nanko K., Hotta N., Suzuki M. Assessing raindrop impact energy at the forest floor in a mature Japanese cypress plantation using continuous raindrop-sizing instruments, Journal of Forest Research. 2004. Vol. 9. Pp. 157 – 164.

Nanko K., Watanabe A., Hotta N., Suzuki M. Physical interpretation of the difference in drop size distributions of leaf drips among tree species, Agricultural and Forest Meteorology. 2013. Vol. 169. Pp. 74 – 84.

Buzdugan L., Stafan S. A comparative study of sodar, lidar wind measurements and aircraft derived wind observations, Romanian Journal of Physics. 2020. Vol. 65. Article 810.

International Civil Aviation Organization (ICAO) Manual on low-level wind shear. Doc 9817 AN/449. First Edition. 2005. (Accessed 19 November 2020)

Little G.G. Acoustic methods for the remote probing of the lower atmosphere, Proceedings of the IEEE. 1969. Vol. 57. Pp. 571 – 578.

METEK Gmbh. PCS.2000-64 Sodar Product Description. (Accessed 19 November 2020). Available:

Tuononen M., O’Connor E.J., Sinclair V.A., Vakkari V. Low-level jets over Uto, Finland, based on Doppler lidar observations, Journal of Applied Meteorology and Climatology. 2017. Vol. 56. Pp. 2577 – 2594.

Van Reeuwijk L.P. (Ed.) Procedures for Soil Analysis; International Soil Reference and Information Centre: Wageningen, The Netherlands. 1992.

Evett S.R. Soil Water Measurement by Time Domain Reflectometry, In Encyclopedia of Water Science; Marcel Dekker, Inc.: New York, NY, USA. 2003.

Lunt I.A., Hubbard S.S., Rubin Y. Soil moisture content estimation using ground-penetrating radar reflection data, Journal of Hydrology. 2005. Vol. 307. Pp. 254 – 269.

Andreasen M., Jensen K.H., Desilets D., Franz T.E., Zreda M., Bogena H.R., Looms M.C. Status and perspectives on the cosmic-ray neutron method for soil moisture estimation and other environmental science applications, Vadose Zone Journal. 2017. Vol. 16. Pp. 1 – 11.

Léger E., Saintenoy A., Coquet Y. Hydrodynamic parameters of a sandy soil determined by ground-penetrating radar inside a single ring infiltrometer, Water Resources Research. 2014. Vol. 50. Pp. 5459 – 5474.

de Jong S.M., Heijenk R.A., Nijland W., van der Meijde M. Monitoring Soil Moisture Dynamics Using Electrical Resistivity Tomography under Homogeneous Field Conditions, Sensors. 2020. Vol. 20. Article 5313.

Vuilleumier L., Meyer A., Stöckli R., Wilbert S., Zarzalejo L.F. Accuracy of satellite-derived solar direct irradiance in Southern Spain and Switzerland, International Journal of Remote Sensing. 2020. Vol. 41. Pp. 8808 – 8838.

SEVIRI. (Accessed 19 November 2020).

Hader D.-P., Cabrol N.A. Monitoring of Solar Irradiance in the High Andes, Photochemistry and Photobiology. 2020. Vol. 96. Pp. 1133 – 1139.

Promplin S., Charoentrakulpeeti W. A trend of surface solar radiation in Chiang Mai, Thailand, IOP Conf. Series: Earth and Environmental Science. 2020. Vol. 538. Article 012025.

Ayik A., Ijumba N., Kabiri C., Goffin P. Estimation of solar resource potential in South Sudan using Heliosat-4 method, 2018 IEEE PES Asia-Pacific Power and Energy Engineering Conference. IEEE. 2018.

de Vos L.W., Droste A.M., Zander M.J., Overeem A., Leijnse H., Heusinkveld B.G., Steeneveld G.J., Uijlenhoet R. Hydrometeorological monitoring using opportunistic sensing networks in the Amsterdam Metropolitan Area, BAMS. 2020. Vol. 101. Pp. E167 – E185.

Oke T.R. Initial guidance to obtain representative meteorological observations at urban sites. World Meteorological Organization Report 86. Instruments and Observing Methods. 2006. (Accessed 19 November 2020). Available:

Balsamo G., Agustì-Parareda A., Albergel C., Arduini G., Beljaars A., Bidlot J., Blyth E., Bousserez N., Boussetta S., Brown A., Buizza R., Buontempo C., Chevallier F., Choulga M., Cloke H., Cronin M.F., Dahoui M., De Rosnay P., Dirmeyer P.A., Drusch M., Dutra E., Ek M.B., Gentine P., Hewitt H., Keeley S.P.E., Kerr Y., Kumar S., Lupu C., Mahfouf J.-F., McNorton J., Mecklenburg S., Mogensen K., Muñoz-Sabater J., Orth R., Rabier F., Reichle R., Ruston B., Pappenberger F., Sandu I., Seneviratne S.I., Tietsche S., Trigo I.F., Uijlenhoet R., Wedi N., Iestyn Woolway R., Zeng X. Satellite and in situ observations for advancing Earth surface modeling: A review, Remote Sensing. 2018. Vol. 10. Article 2038.

McCabe M.F., Rodell M., Alsdorf D.E., Miralles D.G., Uijlenhoet R., Wagner W., Lucieer A., Houborg R., Verhoest N.E.C., Franz T.E., Shi J., Gao H., Wood E.F. The future of Earth observation in hydrology, Hydrology and Earth System Science. 2017. Vol. 21. Pp. 3879 – 3914.

Tauro F., Selker J., van de Giesen N., Abrate T., Uijlenhoet R., Porfiri M., Manfreda S., Caylor K., Moramarco T., Benveniste J., Ciraolo G., Estes L., Domeneghetti A., Perks M.T., Corbari C., Rabiei E., Ravazzani G., Bogena H., Harfouche A., Brocca L., Maltese A., Wickert A., Tarpanelli A., Good S., Alcala J.M.L., Petroselli A., Cudennec C., Blume T., Hut R., Grimaldi S. Measurements and observations in the XXI century (MOXXI): Innovation and multi-disciplinary to sense the hydrological cycle, Hydrological Sciences Journal. 2018. Vol. 63. Pp. 169 – 196.

Zheng F., Tao R., Maier H.R., See L., Savic D., Zhang T., Chen Q., Assumpção T.H., Yang P., Heidari B., Rieckermann J., Minsker B., Bi W., Cai X., Solomatine D., Popescu I. Crowdsourcing methods for data collection in geophysics: State of the art, issues, and future directions, Reviews of Geophysics. 2018. Vol. 56. Pp. 698 – 740.

Allamano P., Croci A., Laio F. Toward the camera rain gauge, Water Resources Research. 2015. Vol. 51. Pp. 1744 – 1757.

Rabiei E., Haberlandt U., Sester M., Fitzner D. Rainfall estimation using moving cars as rain gauges-laboratory experiments, Hydrology and Earth System Science. 2013. Vol. 17. Pp. 4701 – 4712.

Muller C.L., Chapman L., Johnston S., Kidd C., Illingworth S., Foody G., Overeem A., Leigh R.R. Crowdsourcing for climate and atmospheric sciences: Current status and future potential, International Journal of Climatology. 2015. Vol. 35. Pp. 3185 – 3203.

Singh D.K., Jerath H., Raja P. Low cost IoT enabled weather stations, Proceedings of 2020 International conference on Computation, Automation and Knowledge Management (ICCAKM). Amity University. India. 9-11 January 2020. Pp. 31 – 37.

bin Sadli, Dan Darrawi M. An IoT-based smart garden with weather station, 2019 IEEE 9th Symposium on Computer Applications & Industrial Electronics. IEEE. 2019.

Math, Rajinder Kumar M., Dharwadkar N.V. IoT based low-cost weather station and monitoring system for precision agriculture in India, 2018 2nd International conference on IoT in Social, Mobile, Analytics and Cloud (I-SMAC). IEEE. 2018.

Aamer H., Mumtaz R., Anwar H., Poslad S. A very low cost, open, wireless, Internet of Things (IoT) air monitoring platform, 2018 5th International conference on smart cities: improving quality of life using ICT & IoT. IEEE. 2018.

Sushmitha P., Bala G.S. Design and implementation of weather monitoring and controlling system, International Journal of Computer Applications. 2014. Vol. 97. Pp. 19 – 22.

Purnima, Reddy S.R.N. Design of remote monitoring and control system with automatic irrigation system using GSM-Bluetooth, International Journal of Computer Applications. 2012. Vol. 47. Pp. 6 – 13.

Yasas Pansilu Jayasuriya, Chanuka Sandaru Elvitigala, Kolitha Wamakulasooriya, BH Sudantha Low cost and IoT based greenhouse with climate monitoring and controlling system for tropical countries, International conference on System Science and Engineering. IEEE. 2018.

Keawprasert T., Sinhaneti T., Phuuntharo P., Phanakulwijit S., Nimsamer A. Improving the traceability of meteorological measurements at automatic weather stations in Thailand, International Journal of Thermophysics. 2017. Vol. 38. Article 125.

WMO-BIPM workshop on: Measurements Challenges for Global Observation Systems for Climate Change Monitoring, Geneva, Switzerland, 30 Mar 2010. (Accessed 19 November 2020).

Consultative Committee for Thermometry (CCT). (Accessed 19 November 2020).


Devaraju J.T., Suhas K.R., Mohana H.K., Patil V.A. Wireless portable microcontroller based weather monitoring station, Measurement. 2015. Vol. 76. Pp. 189 – 200.

Brosy C., Krampf K., Zeeman M., Wolf B., Junkermann W., Schäfer K., Emeis S., Kunstmann H. Simultaneous multicopter-based air sampling and sensing of meteorological variables, Atmospheric Measurement Techniques. 2017. Vol. 10. Pp. 2773 – 2784.

Konrad T., Hill M., Rowland J., Meyer J. A small, radiocontrolled aircraft as a platform for meteorological sensors, APL Technical Digest. 1970. Vol. 10. Pp. 11 – 19.

Holland G.J., McGeer T., Youngren H. Autonomous aerosondes for economical atmospheric soundings anywhere on the globe, Bulletin of the American Meteorological Society. 1992. Vol. 73. Pp. 1987 – 1998.<1987:aafeas>;2

Spiess T., Bange J., Buschmann M., Vörsmann, P. First application of the meteorological Mini-UAV “M2AV”, Meteorologische Zeitschrift. 2007. Vol. 16. Pp. 159 – 169.

Villa T.F., Gonzalez F., Miljievic B., Ristovski Z.D., Morawska L. An Overview of Small Unmanned Aerial Vehicles for Air Quality Measurements: Present Applications and Future Prospectives, Sensors. 2016. Vol. 16. Article 1072.

Martin S., Bange J., Beyrich F. Meteorological profiling of the lower troposphere using the research UAV “M2AV Carolo”, Atmospheric Measurement Techniques. 2011. Vol. 4. Pp. 705 – 716.

de Boer G., Palo S., Argrow B., LoDolce G., Mack J., Gao R.-S., Telg H., Trussel C., Fromm J., Long C.N., Bland G., Maslanik J., Schmid B., Hock T. The Pilatus unmanned aircraft system for lower atmospheric research, Atmospheric Measurement Techniques. 2016. Vol. 9. Pp. 1845 – 1857.

Aires F., Prigent C., Orlandi E., Milz M., Eriksson P., Crewell S., Lin C.-C., Kangas V. Microwave hyperspectral measurements for temperature and humidity atmospheric profiling from satellite: The clear-sky case, Journal of Geophysical Research: Atmospheres. 2015. Vol. 120. Pp. 11334 – 11351.

Lorenc A.C., Marriott R.T. Predict sensitivity to observations in the Met Office Global numerical weather prediction system, Quarterly Journal of the Royal Meteorological Society. 2014. Vol. 140. Pp. 209 – 224.

Bauer P., Geer A.J., Lopez P., Salmond D. Direct 4D-Var assimilation of all-sky radiances. Part I: Implementation, Tech. Rep., European Centre for Medium–Range Weather Predicts, Reading, U.K. 2010.

Geer A.J. All-sky assimilation: Better snow-scattering radiative transfer and addition of SSMIS humidity sounding channels, Tech. Rep., European Centre for Medium– range Weather Predicts (ECMWF), Reading, U.K. 2013.

Cardinali C. Predict sensitivity to observation (FSO) as a diagnostic tool, Tech. Rep., European Centre for Medium–Range Weather Predicts, Reading, U.K. 2009.

Krishnan P., Kochendorfer J., Dumas E.J., Guillevic P.C., Baker C.B., Meyers T.P., Martos B. Comparison of in-situ, aircraft, and satellite land surface temperature measurements over a NOAA Climate reference network site, Remote Sensing of Environment. 2015. Vol. 165. Pp. 249 – 264.

MODIS. (Accessed 19 November 2020).

Xu L., Abbaszadeh P., Moradkhani H., Chen N., Zhang X. Continental drought monitoring using satellite soil moisture, data assimilation and an integrated drought index, Remote Sensing of Environment. 2020. Vol. 250. Article 112028.

Nesterov V.G. Combustibility of forests and methods to determine it. Moscow: Goslesbumizdat, 1949. 76 P. (In Russian)

State standard R 22.1.09-99. Monitoring and prediction of forest fires. General requirements. Moscow: Gosstandard RF, 1999. 10 P. (In Russian)

Yankovich K.S., Yankovich E.P., Baranovskiy N.V. Classification of Vegetation to Estimate Forest Fire Danger Using Landsat 8 Images: Case Study (2019) Mathematical Problems in Engineering, 2019, article 6296417.

Grishin A.M. Modeling and predicting of disasters. Tomsk: Publishing house of Tomsk University. 2002. 122 P. (In Russian)

Origin Pro. Available: (Accessed 21 December 2020)

RAD Studio. (Accessed 21 December 2020).

Baranovsky N.V. The development of application to software origin pro for informational analysis and predict of forest fire danger caused by thunderstorm activity (2019) Journal of Automation and Information Sciences, 51 (4), pp. 12-23.

Baranovskiy N.V., Yankovich E.P. Geoinformation monitoring of forest fire danger on the basis of remote sensing data of surface by the artificial earth satellite (2015) Journal of Automation and Information Sciences, 47 (8), pp. 11-23.

Matsenko V.V., Sokolov A.Ya., Kalinin S.I., Andriyanova F.I., Andreeva T.A., Ananin S.V., Krylov M.N., Kazantseva L.V. General plan of fire protection. Vol. 1. Explanatory note. 5-99.14-17-PM. Barnaul: State Design and Research Institute "Rosgiproles", Altai branch, 1999. 139 P. (In Russian).

Prichard S.J., Povak N.A., Kennedy M.C., Peterson D.W. Fuel treatment effectiveness in the context of landform, vegetation, and large, wind-driven wildfires (2020) Ecological Applications, 30 (5).

Sanjuan G., Brun C., Margalef T., Cortes A. Wind field uncertainty in forest fire propagation prediction, Procedia Computer Science. 2014. Vol. 29. P. 1535 – 1545.

Valendik E.N. Wind transformation by forest and fire. Dissertation abstract. Krasnoyarsk: Institute of Forest and Timber of the Siberian Branch of the USSR Academy of Sciences. 1966. 26 P. (In Russian),

Lykov A.V. Heat and mass transfer. Minsk: Energy. 1978. 480 P. (In Russian),

Kuznetsov G.V., Baranovskiy N.V. Focused sun's rays and forest fire danger: New concept (2013) Proceedings of SPIE - The International Society for Optical Engineering, 8890.

Baranovskiy N.V. Experimental studies of forest fuel layer ignition by the focused solar radiation, Fire and Explosion Safety. 2012. Vol. 21. N 9. P. 23 - 27. (In Russian).

Zhdanko V.A., Gritsenko M.V. Method of analysis of forest fire seasons, Practical recommendations. Leningrad, 1980. (In Russian).

Baranovskiy N.V., Belikova M.Y., Karanina S.Y., Karanin A.V., Glebova A.V. Methods to estimate lightning activity using WWLLN and RS data (2017) Proceedings of SPIE - The International Society for Optical Engineering, 10466.

Hutchins M.L., Holzworth R.H., Rodger C.J., Brundell J.B. Far-field power of lightning strokes as measured by the World Wide Lightning Location Network, Journal of Atmospheric and Oceanic technology. 2012. Vol. 29. P. 1102 - 1110.

Karanina, S., Kocheeva, N., Belikova, M., Baranovskiy, N., Analysis of a Thunderstorm Activity According to WWLLN: a Case Study, (2018) International Review of Electrical Engineering (IREE), 13 (1), pp. 69-79.

Neshat M., Tabatabi M., Zahmati E., Shirdel M. A hybrid fuzzy knowledge-based system for forest fire risk predicting (2016) International Journal of Reasoning-based Intelligent Systems, 8 (3-4), pp. 132-154.

Razavi-Termeh S.V., Sadeghi-Niaraki A., Choi S.-M. Ubiquitous GIS-based forest fire susceptibility mapping using artificial intelligence methods (2020) Remote Sensing, 12 (10).

Podolskaya A.S., Ershov D.V., Shulyak P.P. Application of the method for assessing the probability of forest fires in the ISDM-Rosleskhoz, Modern problems of remote sensing of the Earth from space. 2011. Vol. 8. N 1. P. 118 - 126. (In Russian).

Farguell A., Cortés A., Margalef T., Miró J.R., Mercader J. Scalability of a multi-physics system for forest fire spread prediction in multi-core platforms (2019) Journal of Supercomputing, 75 (3), pp. 1163-1174.

Taylor S. W., Alexander M. E. Science, technology and human factors in fire danger rating: the Canadian experience, International Journal of Wildland Fire. 2006. Vol. 15, N 1. P. 121-135.


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