UDC 004.9
UDC 332.14
CSCSTI 36.01
CSCSTI 36.33
CSCSTI 87.03
CSCSTI 87.35
By now, a large number of studies have been conducted in the field of sustainable development forecasting, both in terms of single components and on a country-wide scale, including in accordance with UN regulatory documents. However, there is no consensus on the approaches used, the mathematical apparatus and the sustainability criteria. This research attempts to formulate and formalize the task of forecasting sustainable development, as well as to analyse current experience and select the most appropriate method for forecasting the sustainable development of territories with arbitrary composition and mixed organisational structures. The basic categories and baseline scenarios for sustainable development forecasting are proposed. A forecast accuracy of 10–20 % has been selected as required and sufficient. Linear multiple models, panel data, regression-differential models, including those using the support vector machine, optimisation mathematical models, finite difference models, and machine learning methods are considered. Their advantages and disadvantages are noted. Controversial approaches and concepts from a scientific point of view are critically examined, including the time density of regional development. Requirements for forecasting the sustainability of territory development are also presented, along with strategies and scenarios for their improvement. The study leads to the conclusion that the finite-difference model and artificial neural networks are the most promising of the approaches considered. Although, the drawback of artificial neural networks application is the necessity of experimental selection of the model structure for the task to be solved, the possibility of deadlock situations during training, and the unpredictable nature of the results.
geoinformation analysis, natural resource potential, sustainable development, finite difference model, forecasting
1. Zhurkin I.G., Orlov P.Yu., Gruzinov V.S. i dr. Vybor analiticheskoy modeli i kolichestvennyh pokazateley dlya geoinformacionnogo analiza ustoychivogo razvitiya territorial'nyh obrazovaniy // Geodeziya i kartografiya. 2024. T. 85, № 9. S. 46–56. DOIhttps://doi.org/10.22389/0016-7126-2024-1011-9-46-56. https://geocartography.ru/scientific_article/2024_9_46-56?ysclid=m3zkrff9bn652723099
2. Sirotina N.A., Kopoteva A.V., Zatonskiy A.V. Metod konechno-raznostnogo social'no-ekonomicheskogo prognozirovaniya // Prikladnaya matematika i voprosy upravleniya. 2021. № 1. S. 174–189. DOIhttps://doi.org/10.15593/2499-9873/2021.1.10. https://elibrary.ru/download/elibrary_45602172_40061013.pdf
3. Vdovin S.M., Gus'kova N.D., Neretina E.A. i dr. Prognozirovanie ustoychivosti razvitiya regiona na osnove ekonomiko-matematicheskogo modelirovaniya // Nacional'nye interesy: prioritety i bezopasnost'. 2016. № 9(342). S. 18–27.
4. Kasimova T.M. Modeli panel'nyh dannyh kak instrument analiza i prognozirovaniya ekonomicheskih pokazateley regionov RF // Fundamental'nye issledovaniya. 2020. № 3. S. 48–53. DOIhttps://doi.org/10.17513/fr.42698. https://elibrary.ru/download/elibrary_42640772_27654702.pdf
5. Ratnikova T.A. Vvedenie v ekonometricheskiy analiz panel'nyh dannyh // Ekonomicheskiy zhurnal Vysshey shkoly ekonomiki. 2006. T. 10, № 2. S. 267–316.
6. Redmond T., Nasir M.A. Role of natural resource abundance, international trade and financial development in the economic development of selected countries // Resources Policy. 2020. Vol. 66. P. 65–78. DOIhttps://doi.org/10.1016/j.resourpol.2020.101591. https://doi.org/10.1016/j.resourpol.2020.101591
7. Hamdi T., Ben Ali J., Di Costanzo V. Accurate prediction of continuous blood glucose based on support vector regression and differential evolution algorithm // Biocybernetics and Biomedical Engineering. 2018. Vol. 38. P. 362–372. DOIhttps://doi.org/10.1016/j.bbe.2018.02.005. https://doi.org/10.1016/j.bbe.2018.02.005
8. Wang F.K., Du T. Implementing support vector regression with differential evolution to forecast motherboard shipments // Expert Systems with Applications. 2014. Vol. 41. Iss. 8. P. 3850–3855. DOIhttps://doi.org/10.1016/j.eswa.2013.12.022. https://doi.org/10.1016/j.eswa.2013.12.022
9. Sirotina N.A., Kopoteva A.V., Zatonskiy A.V. Primenenie konechno-raznostnyh modeley dlya kratkosrochnogo prognozirovaniya prirodno-resursnogo potenciala Permskogo kraya // Vestnik Yuzhno-Ural'skogo gosudarstvennogo universiteta. Seriya: Komp'yuternye tehnologii, upravlenie, radioelektronika. 2021. T. 21, № 2. S. 154–166. DOIhttps://doi.org/10.14529/ctcr210215. https://elibrary.ru/download/elibrary_45702996_56370217.pdf
10. Goryachev A.A. Modelirovanie dobychi v mirovyh i regional'nyh optimizacionnyh gazovyh modelyah // Problemy ekonomiki i upravleniya neftegazovym kompleksom. 2015. № 6. S. 51–58. DOIhttps://doi.org/10.1016/j.bbe.2018.02.005. https://elibrary.ru/download/elibrary_23592807_15109689.pdf
11. Kuznecov Yu.A., Semenov A.V., Vlasova M.N. Matematicheskoe modelirovanie optimal'nogo ispol'zovaniya nevozobnovimyh prirodnyh resursov // Ekonomicheskiy analiz: teoriya i praktika. 2012. № 32. S. 45–57.
12. Zatonskiy A.V., Sirotina N.A. Upravlenie prirodno-resursnym potencialom Permskogo kraya na osnove konechno-raznostnoy modeli vtorogo poryadka // Vestnik Yuzhno-Ural'skogo gosudarstvennogo universiteta. Seriya: Komp'yuternye tehnologii, upravlenie, radioelektronika. 2022. T. 22, № 2. S. 96–106. DOIhttps://doi.org/10.14529/ctcr220209. https://elibrary.ru/download/elibrary_48408899_41626565.pdf
13. Arabameri A., Pal S.C., Rezaie F., et al. Modeling groundwater potential using novel GIS-based machine-learning ensemble techniques // Journal of Hydrology: Regional Studies. 2021. Vol. 36. P. 100848. DOIhttps://doi.org/10.1016/j.ejrh.2021.100848. https://doi.org/10.1016/j.ejrh.2021.100848
14. Chauhan R., Kaur H., Alankar B. Air Quality Forecast using Convolutional Neural Network for Sustainable Development in Urban Environments // Sustainable Cities and Society. 2021. Vol. 75. P. 103239. DOIhttps://doi.org/10.1016/j.scs.2021.103239. https://doi.org/10.1016/j.scs.2021.103239
15. Chenary K., Pirian Kalat O., Sharifi A. Forecasting sustainable development goals scores by 2030 using machine learning models // Sustainable Development. 2024. Vol. 32. Iss. 6. P. 6520–6538. DOIhttps://doi.org/10.1002/sd.3037. https://onlinelibrary.wiley.com/doi/epdf/10.1002/sd.3037
16. Kopoteva A.V., Maksimov A.A., Sirotina N.A. Modeli mashinnogo obucheniya v zadache prognozirovaniya prirodno-resursnogo potenciala Permskogo kraya // Vestnik Yuzhno-Ural'skogo gosudarstvennogo universiteta. Seriya: Komp'yuternye tehnologii, upravlenie, radioelektronika. 2021. T. 21, № 4. S. 126–136. DOIhttps://doi.org/10.14529/ctcr210411. https://elibrary.ru/download/elibrary_47180492_88260781.pdf
17. Treschevskiy Yu.I., Novikov V.A., Borzakov D.V. Prognozirovanie dinamiki parametrov ustoychivogo eko-socio-ekonomicheskogo razvitiya regionov na osnove metodov empiricheskogo modelirovaniya // Estestvenno-gumanitarnye issledovaniya. 2020. № 32(6). S. 305–310. DOIhttps://doi.org/10.24412/2309-4788-2020-10734. https://elibrary.ru/download/elibrary_44778762_44958169.pdf
18. Volkova S.N., Sivak E.E., Pashkova M.I. i dr. Prognozirovanie regional'nogo razvitiya // Vestnik Kurskoy gosudarstvennoy sel'skohozyaystvennoy akademii. 2015. № 6. S. 9–11.
19. Bushuev V.V., Golubev V.S., Selyukov Yu.G. Energoinformacionnye osnovy ustoychivogo razvitiya (na primere rossiyskih regionov). M.: Energiya, 2005. 58 s.
20. Zatonskiy A.V., Sirotina N.A. Prognozirovanie ekonomicheskih sistem po modeli na osnove regressionnogo differencial'nogo uravneniya // Ekonomika i matematicheskie metody. 2014. T. 50, № 1. S. 91–99.
21. Shatalova O.I. Sovershenstvovanie instrumentariya strategicheskogo planirovaniya i prognozirovaniya regional'nogo razvitiya // Materialy II Ezhegodnoy nauchno-prakticheskoy konferencii Severo-Kavkazskogo federal'nogo universiteta «Universitetskaya nauka — regionu». Stavropol': Fabula, 2014. S. 112–120.
22. Arguchinceva A.V., Arguchincev V.K., Baturin V.A. i dr. Modelirovanie i upravlenie processami regional'nogo razvitiya / pod red. S.N. Vasil'eva. M.: FIZMATLIT, 2001. 432 s.
23. Rudenko L.G. Formirovanie metodicheskogo podhoda k prognozirovaniyu ustoychivogo razvitiya regiona // Problemy rynochnoy ekonomiki. 2024. № 1. S. 46–61. DOIhttps://doi.org/10.33051/2500-2325-2024-1-46-61. https://elibrary.ru/download/elibrary_66019550_43422830.pdf
24. Luukkanen J., Vehmas J., Kaivo-oja J., et al. Towards a General Theory of Sustainable Development: Using a Sustainability Window Approach to Explore All Possible Scenario Paths of Economic Growth and Degrowth // Sustainability. 2024. Vol. 16. Iss. 13. P. 5326. DOIhttps://doi.org/10.3390/su16135326. https://www.mdpi.com/2071-1050/16/13/5326/pdf?version=1719049808
