Multi-output deep learning for high-frequency prediction of air and surface temperature in Kuwait
Salam, M. A. & Mazrooei, S. A. Changing patterns of climate in Kuwait. Asian J. Water Environ. Pollut. 4(1), 119–124 (2007).
Google Scholar
Alkandari, A. J. Climate and climate change aspects of Kuwait. In Terrestrial Environment and Ecosystems of Kuwait: Assessment and Restoration 57–91 (Springer, 2024)
Alahmad, B. et al. Climate change and health in Kuwait: temperature and mortality projections under different climatic scenarios. Environ. Res. Lett. 17(7), 074001 (2022).
Google Scholar
Obradovich, N. & Fowler, J. H. Climate change may alter human physical activity patterns. Nat. Hum. Behav. 1(5), 0097 (2017).
Google Scholar
McMichael, A. J., Woodruff, R. E. & Hales, S. Climate change and human health: present and future risks. The Lancet 367(9513), 859–869 (2006).
Google Scholar
Hepburn, C. & Bowen, A. Prosperity with growth: Economic growth, climate change and environmental limits. In Handbook on Energy and Climate Change 617–638 (Edward Elgar Publishing, 2013)
Omar Asem, S. & Roy, W. Y. Biodiversity and climate change in Kuwait. Int. J. Clim. Change Strateg. Manag. 2(1), 68–83 (2010).
Google Scholar
Quoreshi, A. M. & Madouh, T. A. Kuwait deserts and ecosystems in the context of changing climate. In Terrestrial Environment and Ecosystems of Kuwait: Assessment and Restoration 341–359 (Springer, 2024)
Islam, M. A. & Jacob, S. Major threats to the terrestrial ecosystems of Kuwait and proposed conservation practices. In Terrestrial Environment and Ecosystems of Kuwait: Assessment and Restoration 329–340 (Springer, 2024)
Almutawa, A. A. & Alfraih, A. Impact of climate change on agriculture, fisheries and livestock sectors in Kuwait. J. Geosci. Environ. Prot. 11(10), 141–166 (2023).
Lelieveld, J. et al. Climate change and impacts in the eastern mediterranean and the middle east. Clim. Change 114, 667–687 (2012).
Google Scholar
Waha, K. et al. Climate change impacts in the middle east and northern Africa (mena) region and their implications for vulnerable population groups. Reg. Environ. Change 17, 1623–1638 (2017).
Google Scholar
Zeng, C. Climate Resilience with AI-Powered Weather Forecast. Technical Report (Massachusetts Institute of Technology, 2024).
Schneider, T. et al. Harnessing ai and computing to advance climate modelling and prediction. Nat. Clim. Change 13(9), 887–889 (2023).
Google Scholar
Bordoni, S., Kang, S., Shaw, T. A., Simpson, I. & Zanna, L. The futures of climate modeling. NPJ Clim. Atmos. Sci. 8(1), 99 (2025).
Google Scholar
Smith, B. A., Hoogenboom, G. & McClendon, R. W. Artificial neural networks for automated year-round temperature prediction. Comput. Electron. Agric. 68(1), 52–61 (2009).
Google Scholar
Abdel-Aal, R. E. Hourly temperature forecasting using abductive networks. Eng. Appl. Artif. Intell. 17(5), 543–556 (2004).
Google Scholar
Ustaoglu, B., Cigizoglu, H. & Karaca, M. Forecast of daily mean, maximum and minimum temperature time series by three artificial neural network methods. Meteorol. Appl. 15(4), 431–445 (2008).
Google Scholar
Tasadduq, I., Rehman, S. & Bubshait, K. Application of neural networks for the prediction of hourly mean surface temperatures in Saudi Arabia. Renew. Energy 25(4), 545–554 (2002).
Google Scholar
Hayati, M. & Mohebi, Z. Application of artificial neural networks for temperature forecasting. World Acad. Sci. Eng. Technol. 28(2), 275–279 (2007).
Folland, C. K. et al. High predictive skill of global surface temperature a year ahead. Geophys. Res. Lett. 40(4), 761–767 (2013).
Google Scholar
Fister, D., Pérez-Aracil, J., Peláez-Rodríguez, C., Del Ser, J. & Salcedo-Sanz, S. Accurate long-term air temperature prediction with machine learning models and data reduction techniques. Appl. Soft Comput. 136, 110118 (2023).
Google Scholar
Pérez-Aracil, J. et al. Long-term temperature prediction with hybrid autoencoder algorithms. Appl. Comput. Geosci. 23, 100185 (2024).
Google Scholar
Easterling, W. E. & Stern, P. C. Making Climate Forecasts Matter (National Academies Press, 1999).
Cowtan, K. & Way, R. G. Coverage bias in the hadcrut4 temperature series and its impact on recent temperature trends. Q. J. R. Meteorol. Soc. 140(683), 1935–1944 (2014).
Google Scholar
Waqas, M., Humphries, U. W., Wangwongchai, A., Dechpichai, P. & Ahmad, S. Potential of artificial intelligence-based techniques for rainfall forecasting in Thailand: a comprehensive review. Water 15(16), 2979 (2023).
Google Scholar
Waqas, M., Humphries, U. W., Hlaing, P. T. & Ahmad, S. Seasonal wavenet-lstm: a deep learning framework for precipitation forecasting with integrated large scale climate drivers. Water 16(22), 3194 (2024).
Google Scholar
Waqas, M., Humphries, U. W., Hlaing, P. T., Wangwongchai, A. & Dechpichai, P. Advancements in daily precipitation forecasting: a deep dive into daily precipitation forecasting hybrid methods in the tropical climate of thailand. MethodsX 12, 102757 (2024).
Google Scholar
Uluocak, I. Comparative study of multivariate hybrid neural networks for global sea level prediction through 2050. Environ. Earth Sci. 84(3), 79 (2025).
Google Scholar
Uluocak, I. Hybrid deep learning models for predicting atmospheric methane concentrations: A comparative analysis through 2050. Sci. China Earth Sci. 1, 1–11 (2025).
Khosravi, Y., Ouarda, T. B. & Homayouni, S. Developing an ensemble machine learning framework for enhanced climate projections using cmip6 data in the middle east. NPJ Clim. Atmos. Sci. 8(1), 1–24 (2025).
Google Scholar
Mahmood, A. Y. et al. Climate change prediction on middle east using rnn model and hybrid lstm &gru model. In 2024 Antennas Design and Measurement International Conference (ADMInC) 76–86 (IEEE, 2024).
Elabd, E., Hamouda, H. M., Ali, M. & Fouad, Y. Climate change prediction in Saudi Arabia using a cnn gru lstm hybrid deep learning model in al qassim region. Sci. Rep. 15(1), 1–19 (2025).
Hittawe, M. M., Harrou, F., Togou, M. A., Sun, Y. & Knio, O. Time-series weather prediction in the red sea using ensemble transformers. Appl. Soft Comput. 164, 111926 (2024).
Google Scholar
Alerskans, E., Nyborg, J., Birk, M. & Kaas, E. A transformer neural network for predicting near-surface temperature. Meteorol. Appl. 29(5), 2098 (2022).
Google Scholar
Liu, S. et al. Investigation of a transformer-based hybrid artificial neural networks for climate data prediction and analysis. Front. Environ. Sci. 12, 1464241 (2025).
Google Scholar
Cifuentes, J., Marulanda, G., Bello, A. & Reneses, J. Air temperature forecasting using machine learning techniques: a review. Energies 13(16), 4215 (2020).
Google Scholar
Tran, T. T. K., Bateni, S. M., Ki, S. J. & Vosoughifar, H. A review of neural networks for air temperature forecasting. Water 13(9), 1294 (2021).
Google Scholar
Rühling Cachay, S., Zhao, B., Joren, H. & Yu, R. Dyffusion: A dynamics-informed diffusion model for spatiotemporal forecasting. Adv. Neural Inf. Process. Syst. 36, 45259–45287 (2023).
Thi Kieu Tran, T., Lee, T., Shin, J.-Y., Kim, J.-S. & Kamruzzaman, M. Deep learning-based maximum temperature forecasting assisted with meta-learning for hyperparameter optimization. Atmosphere 11(5), 487 (2020).
Google Scholar
Nandi, A., De, A., Mallick, A., Middya, A. I. & Roy, S. Attention based long-term air temperature forecasting network: Altf net. Knowl.-Based Syst. 252, 109442 (2022).
Google Scholar
Durhasan, T., Pinar, E., Uluocak, I. & Bilgili, M. Future forecast of global mean surface temperature using machine learning and conventional time series methods. Theoret. Appl. Climatol. 156(1), 1–18 (2025).
Google Scholar
Ayad, S. Modeling and forecasting air temperature in tetouan (Morocco) using sarima model. J. Earth Sci. Geotech. Eng. 12, 1–13 (2022).
Shrivastava, V. K., Shrivastava, A., Sharma, N., Mohanty, S. N. & Pattanaik, C. R. Deep learning model for temperature prediction: A case study in New Delhi. J. Forecast. 42(6), 1445–1460 (2023).
Google Scholar
Park, I. et al. Temperature prediction using the missing data refinement model based on a long short-term memory neural network. Atmosphere 10(11), 718 (2019).
Google Scholar
Roy, D. S. Forecasting the air temperature at a weather station using deep neural networks. Procedia Comput. Sci. 178, 38–46 (2020).
Google Scholar
Ribeiro, A. H., Tiels, K., Aguirre, L. A. & Schön, T. Beyond exploding and vanishing gradients: analysing rnn training using attractors and smoothness. In International Conference on Artificial Intelligence and Statistics 2370–2380 (PMLR, 2020).
Arik, S. Ö. & Pfister, T. Tabnet: Attentive interpretable tabular learning. Proc. AAAI Conf. Artif. Intell. 35, 6679–6687 (2021).
Gorishniy, Y., Rubachev, I., Khrulkov, V. & Babenko, A. Revisiting deep learning models for tabular data. Adv. Neural Inf. Process. Syst. 34, 18932–18943 (2021).
Aldughayfiq, B., Ashfaq, F., Jhanjhi, N. & Humayun, M. Explainable ai for retinoblastoma diagnosis: interpreting deep learning models with lime and shap. Diagnostics 13(11), 1932 (2023).
Google Scholar
Altmann, A., Toloşi, L., Sander, O. & Lengauer, T. Permutation importance: a corrected feature importance measure. Bioinformatics 26(10), 1340–1347 (2010).
Google Scholar
link
