GULLY MODELING FOR FOREST RECLAMATION PURPOSES
- Land Reclamation, Recultivation, and Land Protection
Purpose: to develop landslide process modeling methods at the pre-formal study stage.
Materials and methods. The methodology development is based on the author's experience in designing landslide preventive measures, as well as the experience of assessing landslide haz-ard based on available data (visual observations, engineering surveys and regulatory docu-mentation). The theory of environmental engineering, systems approach and cognitive model-ing were used.
Results and discussion. The ideology of cognitive modeling as a basis for pre-formal study of problems (causes) and methods of protecting territories from landslides is proposed and im-plemented. The cognitive map made it possible to solve the problem of identifying or match-ing the real landslide process and methods of protecting territories from landslides with their graphical representation in the form of concepts (graph vertices) and cause-and-effect rela-tionships between them (graph edges), which subsequently allows us to move on to formaliza-tion – the construction of mathematical models (Bayesian belief networks, simultaneous equa-tions systems, path analysis models, etc.). Scenarios for the development of complex situations associated with the imitation of the activity of one or more vertices of the cognitive map which makes it possible to answer the questions “what if ...” and thus provide support in mak-ing decisions on carrying out reclamation measures for landslide protection were obtained.
Conclusions. The obtained cognitive models make it possible to carry out simulation modeling and obtain scenarios for the development of various complex situations. The results can be used in a decision support system for developing models of probabilistic landslide risk management during the formation and operation of engineering structures.
doi: 10.31774/2712-9357-2025-15-2-225-244
landslide, protection of territories, reclamation measures, arable land, natural re-sources, environmental engineering, cognitive map, simulation model
Gorelova G. V., Katsko D. I., Kasko A. I. Cognitive modeling of problems and methods of protecting territories from landslides. Land Reclamation and Hydraulic Engineer-ing. 2025;15(2):225–244. (In Russ.). https://doi.org/10.31774/2712-9357-2025-15-2-225-244.
1. Ivonin V.M., 2022. Meliorativnye sistemy: osnovy obshchey teorii [Reclamation systems: the basics of general theory]. Melioratsiya i gidrotekhnika, vol. 12, no. 1, pp. 119-140, available: https://rosniipm-sm.ru/article?n=1264 [accessed 27.01.2025], DOI: 10.31774/2712-9357-2022-12-1-119-140, EDN: ZECJFV. (In Russian).
2. Kalinin E.V., Kropotkin M.P., 2022. Metody rascheta ustoychivosti sklonov i ot-kosov: rossiyskie podkhody v sopostavlenii s mirovymi tendentsiyami [Methods for calculating the stability of slopes: Russian approaches compared to global trends]. Inzhenernaya geologiya [Engineering Geology], vol. 17, no. 4, pp. 22-38, DOI: 10.25296/1993-5056-2022-17-4-22-38, EDN: HBTWHO. (In Russian).
3. Ivonin V.M., 2024. Teoreticheskaya kontseptsiya agrolesomeliorativnykh sistem [Theoreti-cal concept of agroforestry systems]. Oroshaemoe zemledelie [Irrigated Agriculture], no. 1(44), pp. 59-64, DOI: 10.35809/2618-8279-2024-1-9, EDN: ILDRGH. (In Russian).
4. Axelrod R., 1976. Structure of decision: The Cognitive Maps of Political Elites. Princeton, NJ: Princeton University Press, 404 p.
5. Maksimov V.I., Kornoushenko E.K., 1999. Analiticheskie osnovy primeneniya ko-gnitivnogo podkhoda pri reshenii slabostrukturirovannykh zadach [Analytical foundations for applying the cognitive approach to solving weakly structured problems]. Trudy Instituta prob-lem upravleniya im. V. A. Trapeznikova RAN [Proceed. of Trapeznikov Institute of Control Sciences of the Russian Academy of Sciences], pp. 95-109. (In Russian).
6. Gorelova G.V., Zakharova E.N., Radchenko S.A., 2006. Issledovanie slabostrukturirovannykh problem sotsial'no-ekonomicheskikh sistem: kognitivnyy podkhod: monografiya [Study of weakly structured problems of Socio-Economic Systems: a Cognitive Approach: monograph]. Rostov n/D., SFedU, 332 p., EDN: RSGKOT. (In Russian).
7. Katsko D.I., Katsko A.I., Matsiy V.S., 2024. Kognitivnoe modelirovanie problem melioratsii [Cognitive modeling of land reclamation problems]. Sistemnyy analiz v proektirovanii i upravlenii: sb. nauch. tr. XXVII Mezhdunar. nauchno-prakt. konferentsii [Sys-tems Analysis in Design and Management: Collection of Scientific Papers of the XXVII International Scientific and Practical Conference]. In 2 parts, part 2. St. Petersburg, Polytech-Press Publ., pp. 83-87, DOI: 10.18720/SPBPU/2/id24-150, EDN: FUZBSS. (In Russian).
8. Matsiy S.I., Baziz A., 2020. Gidrologicheskaya klassifikatsiya opolzney Krasnodar-skogo kraya [Hydrological classification of landslides in Krasnodar Territory]. Institutsional'nye preobrazovaniya APK Rossii v usloviyakh global'nykh vyzovov: sb. tez. po materialam V Mezhdunar. konferentsii [Institutional Transformations of the Agro-Industrial Complex of Russia in the Context of Global Challenges: Collection of Abstracts Based on the Proceed. of the V International Conf.]. Krasnodar, Kuban State Agrarian University named after I. T. Trubilin, pp. 14, EDN: TUGZSR. (In Russian).
9. Lamerdonov Z.G., Khashirova T.Yu., Zhirikova I.A., 2025. Sposoby rascheta i usileniya provolochnykh ankernykh sistem, ustroystva dlya ikh ustanovki [Methods for calculating and strengthening wire anchor systems and devices for their installation]. Melioratsiya i gidrotekhnika, vol. 15, no. 1, pp. 232-245, available: https://rosniipm-sm.ru/article?n=1512 [accessed 27.01.2025], DOI: 10.31774/2712-9357-2025-15-1-232-245, EDN: TBFBCD. (In Russian).
10. Degtyarev V.G., Degtyarev G.V., Degtyareva O.G., 2023. Chislennoe modelirovanie i tsifrovoy matematicheskiy analiz pri issledovanii slozhnykh sistem [Numerical modeling and digital mathematical analysis in the study of complex systems]. Izvestiya Nizh-nevolzhskogo agrouniversitetskogo kompleksa: nauka i vysshee professional'noe obrazovanie [Proceed. of the Lower Volga Agro-University Complex: Science and Higher Education], no. 3(71), pp. 540-553, DOI: 10.32786/2071-9485-2023-03-53, EDN: GXGHRA. (In Russian).