Open Access
| Issue |
ITM Web Conf.
Volume 85, 2026
Intelligent Systems for a Sustainable Future (ISSF 2026)
|
|
|---|---|---|
| Article Number | 01002 | |
| Number of page(s) | 7 | |
| Section | AI for Healthcare, Agriculture, Smart Society & Computer Vision | |
| DOI | https://doi.org/10.1051/itmconf/20268501002 | |
| Published online | 09 April 2026 | |
- Oliveira, D. A. B., Guevara Diaz, J. L., Zadrozny, B., & Watson, C. D. (2021). Controlling weather field synthesis using variational autoencoders (ICML Climate Change AI Workshop). [Google Scholar]
- Cai, W., Lan, F., Huang, X., Hao, J., Xia, W., Tang, R., Feng, P., & Li, H. (2024). Generative probabilistic prediction of precipitation-induced landslide deformation with variational autoencoder and gated recurrent unit Frontiers in Earth Science, 12, 1394129. [Google Scholar]
- Jahangir, M. S., & Quilty, J. (2024). Generative deep learning for probabilistic streamflow forecasting: Conditional variational auto-encode. Journal of Hydrology, 629, Article 130498 [Google Scholar]
- Sun, X., Zhang, H., Wang, J., Shi, C., Hua, D., & Li, J. (2022). Ensemble streamflow forecasting based on variational mode decomposition and long short-term memor. Scientific Reports, 12(1), 518. [Google Scholar]
- Wang, X., He, Z., & Peng, X. (2024). Artificial-intelligence-generated content with diffusion models: A literature revie. Mathematics, 12(7), 977. [CrossRef] [MathSciNet] [Google Scholar]
- Harris, L., McRae, A. T. T., Chantry, M., et al. (2022). A generative deep learning approach to stochastic downscaling of precipitation forecast. Journal of Advances in Modeling Earth Systems, 14(??), e2022MS003120. [Google Scholar]
- Szwarcman, D et.al.. (2024). Title of paper: Loss-quantization strategies that improve variational autoencoder synthesis of extreme weather field. Proceedings. [Google Scholar]
- Yin, J., Meo, C., Roy, A., Bou Cher, Z., Wang, Y., Imhoff, R., Uijlenhoet, R., & Dauwels, J. (2024). Precipitation nowcasting using physics informed discriminator generative models. arXiv [Google Scholar]
- Neil et al. (2023) — Unsupervised feature learning for rainfall forecasting, Temporal Forecasting of Rainfall", Journal of Hydroinformatics [Google Scholar]
- Wani, O. A., & colleagues. (2024). Predicting rainfall using machine learning, deep learning, and time series methodologie. Scientific Reports, 14, Article 77687 [Google Scholar]
- W. Hu, H. Zhang, X. Li, and Y. Chen, "Deep learning-based probabilistic precipitation forecasting with uncertainty quantification evaluated using CRPS," Monthly Weather Review, vol. 151, no. 8, pp. 2345–2362, 2023. [Google Scholar]
- L. Main, K. Browning, R. Thompson, and P. Clark, "Skilful probabilistic medium-range precipitation forecasting and evaluation using CRPS and reliability diagnostics," Meteorological Applications, vol. 31, no. 2, 2024. [Google Scholar]
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