From Noise to Precision: A Diffusion-Driven Approach to Zero-Inflated Precipitation Prediction
Files
(Published version)
Date
2025
Authors
Gao, W.
Li, J.
Liu, L.
Le, T.D.
Chen, X.
Du, X.
Liu, J.
Zhao, Y.
Chen, Y.
Editors
Lynce, I.
Murano, N.
Vallati, M.
Villata, S.
Chesani, F.
Milano, M.
Omicini, A.
Dastani, M.
Murano, N.
Vallati, M.
Villata, S.
Chesani, F.
Milano, M.
Omicini, A.
Dastani, M.
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
Frontiers in Artificial Intelligence and Applications, 2025 / Lynce, I., Murano, N., Vallati, M., Villata, S., Chesani, F., Milano, M., Omicini, A., Dastani, M. (ed./s), vol.413, pp.1107-1114
Statement of Responsibility
Wentao Gao, Jiuyong Li, Lin Liu, Thuc Duy Le, Xiongren Chen, Xiaojing Du, Jixue Liu, Yanchang Zhao, Yun Chen
Conference Name
European Conference on Artificial Intelligence, (ECAI), including Conference on Prestigious Applications of Intelligent Systems, (PAIS) (25 Oct 2025 - 30 Oct 2025 : Bologna, Italy)
Abstract
Zero-inflated data pose significant challenges in precipitation forecasting due to the predominance of zeros with sparse non-zero events. To address this, we propose the Zero Inflation Diffusion Framework (ZIDF), which integrates Gaussian perturbation for smoothing zero-inflated distributions, Transformer-based prediction for capturing temporal patterns, and diffusion-based denoising to restore the original data structure. In our experiments, we use observational precipitation data collected from South Australia along with synthetically generated zero-inflated data. Results show that ZIDF demonstrates significant performance improvements over multiple state-of-the-art precipitation forecasting models, achieving up to 56.7% reduction in MSE and 21.1% reduction in MAE relative to the baseline Non-stationary Transformer. These findings highlight ZIDF’s ability to robustly handle sparse time series data and suggest its potential generalizability to other domains where zero inflation is a key challenge.
School/Discipline
Dissertation Note
Provenance
Description
Access Status
Rights
© 2025 The Authors. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).