Grok-Based Temporal Fusion Transformer Framework for Multi-Horizon Coastal Flood Risk Forecasting and Strategic Adaptation Planning

Authors

  • Alexey Mikhaylov

    Financial Faculty, Financial University under the Government of the Russian Federation, Moscow 125167, Russia
    Department of Science, Baku Eurasian University, Baku AZ 1073, Azerbaijan
  • Sergey Barykin

    Graduate School of Service and Trade, Peter the Great St. Petersburg Polytechnic University, St. Petersburg 195251, Russia
  • Daria Dinets

    Department of Finance, Accounting, and Auditing, Peoples' Friendship University of Russia named after Patrice Lumumba, Moscow 125167, Russia
  • Akram Ochilov

    Department of Economics, Karshi State University, Karshi 180119, Uzbekistan
  • Alfiya Kuznetsova

    Department of Economics, Bashkir State Agrarian University, Ufa 450001, Russia
  • Jamshid Tukhtabaev

    Department of Finance and Accounting, Graduate School of Business and Entrepreneurship under The Cabinet of Ministers of The Republic of Uzbekistan, Tashkent 100000, Uzbekistan
  • Aslitdin Nizamov

    Bukhara Engineering Technological Institute, Bukhara 200100, Uzbekistan
  • Nodira Murodova

    Department of Regional Economics, Navoi State University, Navoi 210100, Uzbekistan
  • Nasiba Ashurova

    Department of Economy & Management, Navoi State University of Mining and Technologies, Navoi 210100, Uzbekistan
  • Tomonobu Senjyu

    Department of Electrical and Electronics Engineering, Faculty of Engineering, University of the Ryukyus, Okinawa 903-0213, Japan
  • Valery Abramov

    Institute for Research of International Economic Relations, Financial University under the Government of the Russian Federation, Moscow 125167, Russia
  • N. B. A. Yousif

    Department of Sociology, College of Humanities and Science, Ajman University, Ajman P.O. Box 346, United Arab Emirates

    Humanities and Social Sciences Research Centre (HSSRC), Ajman University, Ajman P.O. Box 346, United Arab Emirates

DOI:

https://doi.org/10.30564/re.v8i1.12824
Received: 27 November 2025 | Revised: 31 December 2025 | Accepted: 15 January 2026 | Published Online: 13 February 2026

Abstract

The optimized Grok algorithm can significantly improve the accuracy of time series analysis and understanding the dynamics of climate change. Fine-tuned Grok architecture can be used to monitor and analyze climate processes. The main aim is to analyze the Fine-tuned Grok architecture for research on climate change, world ecology, carbon dioxide growth, and carbon funds. The global challenges of climate change and ecological degradation demand innovative analytical approaches capable of processing vast, multivariate, and non-linear datasets. Concurrently, the global financial system, deeply intertwined with energy transitions and sustainable development, requires sophisticated tools for risk assessment and investment strategy in a changing world. Fine-tuned Grok architecture model helps to plan strategies for adaptation to climate change by calculating the optimal allocation of resources, taking into account risks and reducing losses. Due to its ability to respond quickly to new conditions, the system will be able to quickly adjust evacuation plans, deploy protective structures, and distribute assistance to affected regions. The use of artificial intelligence significantly expands the capabilities of the scientific community and authorities in monitoring, assessing, and managing climate change. The optimized Fine-tuned Grok architecture opens the way to a new level of informed decision-making about climate change and ensuring the safety of our future generations.

Keywords:

AI; Grok; Climate; Change Environmental Protection; Ecosystem Sustainability; Biological Diversity; Environmental Disasters; Air and Water Pollution

References

[1] Zhunussova, G.Z., Ratner, S.V., Zhunussova, G.Z., et al., 2020. Renewable energy in Kazakhstan: challenges and prospects. International Energy Journal. 20(3), 311–324. Available from: https://www.researchgate.net/publication/368685020_Renewable_Energy_in_Kazakhstan_Challenges_and_Prospects

[2] Zhou, G., Ji, Y., Chen, X., et al., 2018. Artificial neural networks and the mass appraisal of real estate. International Journal of Online and Biomedical Engineering (iJOE). 14(3), 180–187. DOI: https://doi.org/10.3991/ijoe.v14i03.8420

[3] Zhang, H., Jin, C., Bouri, E., et al., 2023. Realized higher-order moments spillovers between commodity and stock markets: Evidence from China. Journal of Commodity Markets. 30, 100275. DOI: https://doi.org/10.1016/j.jcomm.2022.100275

[4] Zhanbayev, R.A., Yerkin, A.Y., Shutaleva, A.V., et al., 2023. State asset management paradigm in the quasi-public sector and environmental sustainability: Insights from the Republic of Kazakhstan. Frontiers in Environmental Science. 10, 1037023. DOI: https://doi.org/10.3389/fenvs.2022.1037023

[5] Zhamiyeva, R., Sultanbekova, G., Balgimbekova, G., et al., 2022. Problems of the effectiveness of the implementation of international agreements in the field of waste management: The study of the experience of Kazakhstan in the context of the applicability of European legal practices. International Environmental Agreements: Politics, Law and Economics. 22(1), 177–199. DOI: https://doi.org/10.1007/s10784-021-09549-0

[6] Zerveas, G., Jayaraman, S., Patel, D., et al., 2021. A Transformer-based Framework for Multivariate Time Series Representation Learning. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, Online, 14 August 2021; pp. 2114–2124. DOI: https://doi.org/10.1145/3447548.3467401

[7] Zakharov, A.V., Kharlamov, A.V., 2010. Geocoding technique in constructing geographically weighted regression models for mass appraisal under conditions of uncertainty and heterogeneity of initial data. Property relations in the Russian Federation. 7, 76–85.

[8] Yesbergen, R.A., Yessengaziyeva, S.K., Asrepov, G.N., 2022. State regulation of agricultural industry of the Republic of Kazakhstan in conditions of VUCA-world. Problems of AgriMarket. 2, 71–77. DOI: https://doi.org/10.46666/2022-2.2708-9991.07

[9] Yerkinbayeva, L., Teleuyev, G., Kalymbek, B., et al., 2021. Legal Regulation of Kazakhstan’s Transition to the Green Economy. Journal of Environmental Management and Tourism. 12(5), 1335. DOI: https://doi.org/10.14505//jemt.12.5(53).18

[10] Yasnitsky, L.N., Yasnitsky, V.L., Alekseev, A.O., 2021. The complex neural network model for mass appraisal and scenario forecasting of the urban real estate market value that adapts itself to space and time. Complexity. 2021(1), 5392170. DOI: https://doi.org/10.1155/2021/5392170

[11] Xu, S., Shao, M., Qiao, W., et al., 2018. Generalized AIC method based on higher-order moments and entropy of financial time series. Physica A: Statistical Mechanics and its Applications. 505, 1127–1138. DOI: https://doi.org/10.1016/j.physa.2018.04.048

[12] Xu, M., Shang, P., 2018. Analysis of financial time series using multiscale entropy based on skewness and kurtosis. Physica A: Statistical Mechanics and Its Applications. 490, 1543–1550. DOI: https://doi.org/10.1016/j.physa.2017.08.136

[13] Wu, N., Green, B., Ben, X., et al., 2020. Deep transformer models for time series forecasting: The influenza prevalence case. arXiv preprint. arXiv: 2001.08317. DOI: https://doi.org/10.48550/ARXIV.2001.08317

[14] Worzala, E., Lenk, M., Silva, A., 1995. An exploration of neural networks and its application to real estate valuation. Journal of Real Estate Research. 10(2), 185–201. DOI: https://doi.org/10.1080/10835547.1995.12090782

[15] Werner, F., Sotskov, Y.N., 2006. Mathematics of Economics and Business. Routledge: London, UK. DOI: https://doi.org/10.4324/9780203401385

[16] Lin, X., Fu, Y., Peng, D.Z., et al., 2024. CFD- and BPNN-based investigation and prediction of air pollutant dispersion in urban environment. Sustainable Cities and Society. 100, 105029. DOI: https://doi.org/10.1016/j.scs.2023.105029

[17] Wang, Q., Guo, J., Li, R., 2023a. Spatial spillover effects of official development assistance on environmental pressure in sub-Saharan African (SSA) countries. Geography and Sustainability. 4(2), 170–178. DOI: https://doi.org/10.1016/j.geosus.2023.03.004

[18] Wang, Q., Hu, S., Li, L., et al., 2023b. Accelerating urbanization serves to reduce income inequality without sacrificing energy efficiency – Evidence from the 78 countries. Sustainable Cities and Society. 92, 104477. DOI: https://doi.org/10.1016/j.scs.2023.104477

[19] Wang, Q., Guo, J., Li, R., et al., 2024. Economic growth and carbon emission in the Organization for Economic Cooperation and Development countries: The effects of oil, gas, and renewable energy. Energy & Environment. 35(4), 2107–2130. DOI: https://doi.org/10.1177/0958305X221138817

[20] Wang, M.-C., Chang, T., Mikhaylov, A., et al., 2024. A measure of quantile-on-quantile connectedness for the US treasury yield curve spread, the US Dollar, and gold price. The North American Journal of Economics and Finance. 74, 102232. DOI: https://doi.org/10.1016/j.najef.2024.102232

[21] Wang, D., Li, V.J., 2019. Mass appraisal models of real estate in the 21st century: A systematic literature review. Sustainability. 11(24), 7006. DOI: https://doi.org/10.3390/su11247006

[22] Walther, G.-R., Post, E., Convey, P., et al., 2002. Ecological responses to recent climate change. Nature. 416(6879), 389–395. DOI: https://doi.org/10.1038/416389a

[23] Tutumlu, A., Rustemov, I., 2021. The paradox of authoritarian power: bureaucratic games and information asymmetry. The case of Nazarbayev’s Kazakhstan. Problems of Post-Communism. 68(2), 124–134. DOI: https://doi.org/10.1080/10758216.2019.1699432

[24] Toms, B.A., Barnes, E.A., Ebert‐Uphoff, I., 2020. Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability. Journal of Advances in Modeling Earth Systems. 12(9), e2019MS002002. DOI: https://doi.org/10.1029/2019MS002002

[25] Teng, Y., Shang, P., 2018. Detrended fluctuation analysis based on higher-order moments of financial time series. Physica A: Statistical Mechanics and Its Applications. 490, 311–322. DOI: https://doi.org/10.1016/j.physa.2017.08.062

[26] Tasmaganbetov, A.B., Кunurkulzhayeva, G.T., Imanbayeva, Z.O., et al., 2020. Future development of price instruments of state support for the use of renewable energy sources in Kazakhstan. International Journal of Energy Economics and Policy. 10(1), 140–144. DOI: https://doi.org/10.32479/ijeep.8481

[27] Tang, Y., Chen, P., 2014. Time varying moments, regime switch, and crisis warning: The birth–death process with changing transition probability. Physica A: Statistical Mechanics and Its Applications. 404, 56–64. DOI: https://doi.org/10.1016/j.physa.2014.02.038

[28] Taleb, N., 1998. Dynamic Hedging: Managing Vanilla and Exotic Options. Wiley: London, UK.

[29] Tabachnick, B.G., Fidell, L.S., 2019. Using Multivariate Statistics. Pearson: London, UK.

[30] Steffen, W., Rockström, J., Richardson, K., et al., 2018. Trajectories of the Earth System in the Anthropocene. Proceedings of the National Academy of Sciences. 115(33), 8252–8259. DOI: https://doi.org/10.1073/pnas.1810141115

[31] Song, X., Wang, Q., Li, R., et al., 2023. A novel evaluation framework for China’s Environmental-Economic impacts by global trade. Environmental Impact Assessment Review. 101, 107133. DOI: https://doi.org/10.1016/j.eiar.2023.107133

[32] Soltangazinov, A., Smagulova, Z., Amirova, M., et al., 2020. Energy Efficiency as a Factor of Sustainable Development in Kazakhstan. International Journal of Energy Economics and Policy. 10(1), 325–330. DOI: https://doi.org/10.32479/ijeep.8618

[33] Skachkova, M.E., Gureva, O.S., 2022. Information support of urban planning activities in Russia. Geodesy and Cartography. 986(8), 45–55. DOI: https://doi.org/10.22389/0016-7126-2022-986-8-45-55

[34] Skachkova, M.E., Guryeva, O.S., 2023. Monitoring of the State of Saint Petersburg Green Spaces by Remote Sensing Data. Ecology and Industry of Russia. 27(5), 51–57. DOI: https://doi.org/10.18412/1816-0395-2023-5-51-57

[35] Siregar, R., Pontines, V., Rajan, R., 2004. Extreme value theory and the incidence of currency crises. Econometric Society 2004 Australasian Meetings. 181. Available from: https://ideas.repec.org/p/ecm/ausm04/181.html

[36] Singh, R.K., Neuert, C.E., Raykov, T., 2024. Assessing conceptual comparability of single-item survey instruments with a mixed-methods approach. Quality & Quantity. 58(4), 3303–3329. DOI: https://doi.org/10.1007/s11135-023-01801-w

[37] Shaimenova, A., Ilyassova, G., Klyuyeva, Y., et al., 2020. Development of the Institution of Arbitration in Kazakhstan: Problems of Theory and Practice. Journal of Advanced Research in Law and Economics. 11(1), 169. DOI: https://doi.org/10.14505//jarle.v11.1(47).21

[38] Shah, F.A., Mikhaylov, A., Haq, E.U., 2025. Numerical framework for investigating MHD heat and mass transfer in nanofluid flow over 2-D boundary layers in a porous medium: A variation of parameters method approach. Results in Engineering. 25, 103547. DOI: https://doi.org/10.1016/j.rineng.2024.103547

[39] Serebrennikova, A.V., Minyaseva, T.F., Kala, N.S., et al., 2020. Comparative Analysis of Foundations of Legal Regulation of Criminal Liability for Organ Trafficking in the Russian Federation, Kazakhstan, and the European Union. Journal of Advanced Research in Law and Economics. 11(4), 1405. DOI: https://doi.org/10.14505/jarle.v11.4(50).37

[40] Seitzhanov, S., Kurmanov, N., Petrova, M., et al., 2020. Stimulation of entrepreneurs’ innovative activity: evidence from Kazakhstan. Entrepreneurship and Sustainability Issues. 7(4), 2615–2629. DOI: https://doi.org/10.9770/jesi.2020.7.4(4)

[41] Scher, S., Messori, G., 2018. Predicting weather forecast uncertainty with machine learning. Quarterly Journal of the Royal Meteorological Society. 144(717), 2830–2841. DOI: https://doi.org/10.1002/qj.3410

[42] Scheffers, B.R., de Meester, L., Bridge, T.C.L., et al., 2016. The broad footprint of climate change from genes to biomes to people. Science. 354(6313), aaf7671. DOI: https://doi.org/10.1126/science.aaf7671

[43] Sawant, R., Jangid, Y., Tiwari, T., et al., 2018. Comprehensive analysis of housing price prediction in pune using multi-featured random forest approach. In Proceedings of the 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), Pune, India, 16 August 2018; pp. 1–5. DOI: https://doi.org/10.1109/ICCUBEA.2018.8697402

[44] Yang, J., Liu, Z., Yu, Q., et al., 2024. Estimation of global transpiration from remotely sensed solar-induced chlorophyll fluorescence. Remote Sensing of Environment. 303, 113998. DOI: https://doi.org/10.1016/j.rse.2024.113998

[45] Lymer, A., Hasseldine, J. (Eds.), 2002. The International Taxation System. Springer: Boston, MA, USA. DOI: https://doi.org/10.1007/978-1-4615-1071-0

[46] Runge, J., Bathiany, S., Bollt, E., et al., 2019. Inferring causation from time series in Earth system sciences. Nature Communications. 10(1), 2553. DOI: https://doi.org/10.1038/s41467-019-10105-3

[47] Rolnick, D., Donti, P.L., Kaack, L.H., et al., 2023. Tackling climate change with machine learning. ACM Computing Surveys. 55(2), 1–96. DOI: https://doi.org/10.1145/3485128

[48] Reichstein, M., Camps-Valls, G., Stevens, B., et al., 2019. Deep learning and process understanding for data-driven Earth system science. Nature. 566(7743), 195–204. DOI: https://doi.org/10.1038/s41586-019-0912-1

[49] Ravikumar, A.S., 2017. Real Estate Price Prediction Using Machine Learning [Master’s Thesis]. National College of Ireland: Dublin, Ireland. Available from: https://norma.ncirl.ie/3096/1/aswinsivamravikumar.pdf

[50] Rapposelli, A., Birindelli, G., Modina, M., 2024. The relationship between firm size and efficiency: why does default on bank loans matter? Quality & Quantity. 58(4), 3379–3401. DOI: https://doi.org/10.1007/s11135-023-01810-9

[51] Rahman, Md.M., Mikhaylov, A., Bhatti, I., 2024. The impact of investment in human capital on investment efficiency: a PLS-SEM approach in the context of Bangladesh. Quality & Quantity. 58(5), 4959–4986. DOI: https://doi.org/10.1007/s11135-024-01889-8

[52] Raguzin, I.I., Bykova, E.N., Lepikhina, O.Yu., 2023. Polygonal Metric Grid Method For Estimating The Cadastral Value of Land Plots. Lomonosov Geography Journal. 78(3), 92–103. DOI: https://doi.org/10.55959/MSU0579-9414.5.78.3.8

[53] Pravdina E. A., Lepikhina O., 2019. Variable accounting of pricing factors at land parcels cadastral valuation (on the example of Saint-Petersburg). Bulletin of the Tomsk Polytechnic University. 330(2), 65–74. DOI: https://doi.org/10.18799/24131830/2019/2/94 (in Russian)

[54] Pieper, M., 2021. The linchpin of Eurasia: Kazakhstan and the Eurasian economic union between Russia’s defensive regionalism and China’s new Silk Roads. International Politics. 58(3), 462–482. DOI: https://doi.org/10.1057/s41311-020-00244-6

[55] Petrenko, Y., Denisov, I., Koshebayeva, G., et al., 2020. Energy Efficiency of Kazakhstan Enterprises: Unexpected Findings. Energies. 13(5), 1055. DOI: https://doi.org/10.3390/en13051055

[56] Pervukhin, D., Davardoost, H., Kotov, D., et al., 2023. A sustainable development goals-based mathematical model for selecting oil and gas investment projects under uncertainty and limited resources. Advanced Mathematical Models & Applications. 8(3), 502–528. Available from: https://jomardpublishing.com/UploadFiles/Files/journals/AMMAV1N1/V8N3/Pervukhin_et_al.pdf

[57] Pecl, G.T., Araújo, M.B., Bell, J.D., et al., 2017. Biodiversity redistribution under climate change: Impacts on ecosystems and human well-being. Science. 355(6332), eaai9214. DOI: https://doi.org/10.1126/science.aai9214

[58] Pázman A., 2001. Linearization of Nonlinear Regression Models by Smoothing. Tatra Mountains Mathematical Publications. 22, 13–25.

[59] Pavlova, V.A., Sulin, M.A., Lepikhina, O.Y., 2019. The mathematical modelling of the land resources mass evaluation in agriculture. Journal of Physics: Conference Series. 1333(3), 032049. DOI: https://doi.org/10.1088/1742-6596/1333/3/032049

[60] Parmesan, C., Yohe, G., 2003. A globally coherent fingerprint of climate change impacts across natural systems. Nature. 421(6918), 37–42. DOI: https://doi.org/10.1038/nature01286

[61] Pan, G.-G., Shiu, Y.-M., Wu, T.-C., 2022. Can risk-neutral skewness and kurtosis subsume the information content of historical jumps? Journal of Financial Markets. 57, 100614. DOI: https://doi.org/10.1016/j.finmar.2020.100614

[62] Ozenbayeva, A., Yerezhepkyzy, R., Yessetova, S., et al., 2022. Legal regulation of transboundary water resources of the Republic of Kazakhstan. Environmental Development. 44, 100781. DOI: https://doi.org/10.1016/j.envdev.2022.100781

[63] Osiyevskyy, O., Dewald, J., 2018. The pressure cooker: When crisis stimulates explorative business model change intentions. Long Range Planning. 51(4), 540–560. DOI: https://doi.org/10.1016/j.lrp.2017.09.003

[64] Oreshkin, B.N., Carpov, D., Chapados, N., et al., 2019. N-BEATS: Neural basis expansion analysis for interpretable time series forecasting. arXiv preprint. arXiv: 1905.10437. DOI: https://doi.org/10.48550/ARXIV.1905.10437

[65] Robison, A.L., Koenig, L.E., Potter, J.D., et al., 2024. Lotic‐SIPCO2 : Adaptation of an open‐source CO2 sensor system and examination of associated emission uncertainties across a range of stream sizes and land uses. Limnology and Oceanography: Methods. 22(4), 191–207. DOI: https://doi.org/10.1002/lom3.10600

[66] Ma, Y., Pan, Y., Zhang, C., et al., 2024. Improved estimates of sub‐regional groundwater storage anomaly using coordinated forward modeling. Water Resources Research. 60(7), e2023WR036105. DOI: https://doi.org/10.1029/2023WR036105

[67] Mikhaylov, A., Yousif, N.B.A., Dincer, H., et al., 2025. Analysis of the knowledge and innovation-based customer expectations for the green crypto assets in investment strategies using artificial intelligence and facial expression-based fuzzy modelling. Quality & Quantity. 59(3), 2861–2890. DOI: https://doi.org/10.1007/s11135-025-02098-7

[68] Mikhaylov, A., Yousif, N.B.A., An, J., 2025. How does the high efficiency of the Chilean stock market affect the energy transition? A study based on the Deep seek optimization algorithm. Finance: Theory and Practice. 29(1), 181–194. DOI: https://doi.org/10.26794/2587-5671-2025-29-1-181-194 (in Russian)

[69] Mikhaylov, A., Bhatti, M.I.M., 2025. The link between DFA portfolio performance, AI financial management, GDP, government bonds growth and DFA trade volumes. Quality & Quantity. 59(1), 339–356. DOI: https://doi.org/10.1007/s11135-024-01940-8

[70] McDermott, P.L., Wikle, C.K., 2019. Bayesian recurrent neural network models for forecasting and quantifying uncertainty in spatial-temporal data. Entropy. 21(2), 184. DOI: https://doi.org/10.3390/e21020184

[71] Matrokhina, K., Trofimets, V., Mazakov, E., et al., 2023. Development of methodology for scenario analysis of investment projects of enterprises of the mineral resource complex. Journal of Mining Institute. 259, 112–124. DOI: https://doi.org/10.31897/PMI.2023.3

[72] Massel, L., Komendantova, N., Massel, A., et al., 2024. Resilience of socio-ecological and energy systems: Intelligent information technologies for risk assessment of natural and technogenic threats. Journal of Infrastructure Policy and Development. 8(7), 4700. DOI: https://doi.org/10.24294/jipd.v8i7.4700

[73] Ma, L., Liu, Y., Zhang, X., et al., 2019. Deep learning in remote sensing applications: A meta-analysis and review. ISPRS Journal of Photogrammetry and Remote Sensing. 152, 166–177. DOI: https://doi.org/10.1016/j.isprsjprs.2019.04.015

[74] Liu, Y., Racah, E., Prabhat, et al., 2016. Application of Deep Convolutional Neural Networks for Detecting Extreme Weather in Climate Datasets. arXiv preprint. arXiv: 1605.01156. DOI: https://doi.org/10.48550/ARXIV.1605.01156

[75] Litvinenko, V., Petrov, E., Vasilevskaya, D., et al., 2022. Assessment of the role of the state in the management of mineral resources. Journal of Mining Institute. 259, 95–111. DOI: https://doi.org/10.31897/PMI.2022.100

[76] Lim, B., Arık, S.Ö., Loeff, N., et al., 2021. Temporal Fusion Transformers for interpretable multi-horizon time series forecasting. International Journal of Forecasting. 37(4), 1748–1764. DOI: https://doi.org/10.1016/j.ijforecast.2021.03.012

[77] Lenton, T.M., Rockström, J., Gaffney, O., et al., 2019. Climate tipping points—Too risky to bet against. Nature. 575(7784), 592–595. DOI: https://doi.org/10.1038/d41586-019-03595-0

[78] Lees, T., Tseng, G., Atzberger, C., et al., 2022. Deep Learning for Vegetation Health Forecasting: A Case Study in Kenya. Remote Sensing. 14(3), 698. DOI: https://doi.org/10.3390/rs14030698

[79] Wu, Y., Xue, F., Li, M., et al., 2024. A novel Building Section Skeleton for compact 3D reconstruction from point clouds: A study of high-density urban scenes. ISPRS Journal of Photogrammetry and Remote Sensing. 209, 85–100. DOI: https://doi.org/10.1016/j.isprsjprs.2024.01.020

[80] Krechko, O., Mikhaylov, A., 2025. Global electricity generation from renewable sources using fuzzy sets and spatial analysis: revolution in solar and wind energy in BRICS countries. Quality & Quantity. 59(2), 1553–1571. DOI: https://doi.org/10.1007/s11135-024-02033-2

[81] Kondrashov, D., Chekroun, M.D., Ghil, M., 2018. Data-adaptive harmonic decomposition and prediction of Arctic sea ice extent. Dynamics and Statistics of the Climate System. 3(1). DOI: https://doi.org/10.1093/climsys/dzy001

[82] Kadow, C., Hall, D.M., Ulbrich, U., 2020. Artificial intelligence reconstructs missing climate information. Nature Geoscience. 13(6), 408–413. DOI: https://doi.org/10.1038/s41561-020-0582-5

[83] Lawton, N., Kumar, A., Thattai, G., et al., 2023. Neural Architecture Search for Parameter-Efficient Fine-tuning of Large Pre-trained Language Models. In Proceedings of the Findings of the Association for Computational Linguistics: ACL 2023, Association for Computational Linguistics, Toronto, ON, Canada, 9–14 July 2023; pp. 8506–8515. DOI: https://doi.org/10.18653/v1/2023.findings-acl.539

[84] Shah Irshad, A., Naseer Zakir, M., Shah Rashad, S., et al., 2024. Comparative analyses and optimizations of hybrid biomass and solar energy systems based upon a variety of biomass technologies. Energy Conversion and Management: X. 23, 100640. DOI: https://doi.org/10.1016/j.ecmx.2024.100640

[85] Irrgang, C., Boers, N., Sonnewald, M., et al., 2021. Towards neural Earth system modelling by integrating artificial intelligence in Earth system science. Nature Machine Intelligence. 3(8), 667–674. DOI: https://doi.org/10.1038/s42256-021-00374-3

[86] Intergovernmental Panel on Climate Change (IPCC), 2023. Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC: Geneva, Switzerland. DOI: https://doi.org/10.59327/IPCC/AR6-9789291691647

[87] Hewamalage, H., Ackermann, K., Bergmeir, C., 2023. Forecast evaluation for data scientists: common pitfalls and best practices. Data Mining and Knowledge Discovery. 37(2), 788–832. DOI: https://doi.org/10.1007/s10618-022-00894-5

[88] Hansen, J., Sato, M., Ruedy, R., 2012. Perception of climate change. Proceedings of the National Academy of Sciences. 109(37). DOI: https://doi.org/10.1073/pnas.1205276109

[89] Ham, Y.-G., Kim, J.-H., Luo, J.-J., 2019. Deep learning for multi-year ENSO forecasts. Nature. 573(7775), 568–572. DOI: https://doi.org/10.1038/s41586-019-1559-7

[90] Galala, A.M., Nazir, U., Ayadi, M., et al., 2025. Predicting simulations of entropy generation with multiple nanoscales towards cylinder/surface engaging non-Fourier law: Energy systems and power generation. Results in Engineering. 26, 105428. DOI: https://doi.org/10.1016/j.rineng.2025.105428

[91] Shi, N., Chen, K., Zhou, G., et al., 2020. A Feature Space Constraint-Based Method for Change Detection in Heterogeneous Images. Remote Sensing. 12(18), 3057. DOI: https://doi.org/10.3390/rs12183057

[92] Diffenbaugh, N.S., Burke, M., 2019. Global warming has increased global economic inequality. Proceedings of the National Academy of Sciences. 116(20), 9808–9813. DOI: https://doi.org/10.1073/pnas.1816020116

[93] Secchi, D., Grimm, V., Herath, D.B., et al., 2024. Modeling and theorizing with agent-based sustainable development. Environmental Modelling & Software. 171, 105891. DOI: https://doi.org/10.1016/j.envsoft.2023.105891

[94] Bonavita, M., Laloyaux, P., 2020. Machine Learning for Model Error Inference and Correction. Journal of Advances in Modeling Earth Systems. 12(12), e2020MS002232. DOI: https://doi.org/10.1029/2020MS002232

[95] An, J., Mikhaylov, A., Chang, T., 2024. Relationship between the popularity of a platform and the price of NFT assets. Finance Research Letters. 61, 105057. DOI: https://doi.org/10.1016/j.frl.2024.105057

[96] An, J., Mikhaylov, A., 2024. Technology-based forecasting approach for recognizing trade-off between time-to-market reduction and devising a scheduling process in open innovation management. Journal of Open Innovation: Technology, Market, and Complexity. 10(1), 100207. DOI: https://doi.org/10.1016/j.joitmc.2024.100207

[97] Allen, M.R., Dube, O.P., Solecki, W., et al., 2018. Special Report: Global Warming of 1.5 °C—Framing and Context. IPCC: Geneva, Switzerland. Available from: https://www.ipcc.ch/sr15/chapter/chapter-1/

[98] Dimitriadis, K.A., Koursaros, D., Savva, C.S., 2024. The influence of the “environmental-friendly” character through asymmetries on market crash price of risk in major stock sectors. Journal of Climate Finance. 9, 100052.

[99] Dimitriadis, K.A., Koursaros, D., Savva, C.S., 2025. The influential impacts of international dynamic spillovers in forming investor preferences: A quantile-VAR and GDCC-GARCH perspective. Applied Economics. 57(45), 7175–7195. DOI: https://doi.org/10.1080/00036846.2024.2387868

[100] Ul‐Durar, S., Dimitriadis, K.A., Arshed, N., et al., 2025. Distributional and Tail‐Dependent Perspectives in Economic Relationships: A Review of Quantile Regression Application. Journal of Economic Surveys. joes.70057. DOI: https://doi.org/10.1111/joes.70057

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Mikhaylov, A., Barykin, S., Dinets, D., Ochilov, A., Kuznetsova, A., Tukhtabaev, J., Nizamov, A., Murodova, N., Ashurova, N., Senjyu, T., Abramov, V., & Yousif , N. B. A. (2025). Grok-Based Temporal Fusion Transformer Framework for Multi-Horizon Coastal Flood Risk Forecasting and Strategic Adaptation Planning. Research in Ecology, 8(1), 311–329. https://doi.org/10.30564/re.v8i1.12824

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Vol.8, Iss.1 (February 2026)

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Article (This article belongs to the Special Issue "Innovative application of AI and machine learning in solving ecological problems")

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Copyright © 2025 Alexey Mikhaylov, Sergey Barykin, Daria Dinets, Akram Ochilov, Alfiya Kuznetsova, Jamshid Tukhtabaev, Aslitdin Nizamov, Nodira Murodova, Nasiba Ashurova, Tomonobu Senjyu, Valery Abramov, N. B. A. Yousif

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This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

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