{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:35:49Z","timestamp":1761176149285,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>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\u2019s ability to robustly handle sparse time series data and suggest its potential generalizability to other domains where zero inflation is a key challenge.<\/jats:p>","DOI":"10.3233\/faia250921","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:46:05Z","timestamp":1761126365000},"source":"Crossref","is-referenced-by-count":0,"title":["From Noise to Precision: A Diffusion-Driven Approach to Zero-Inflated Precipitation Prediction"],"prefix":"10.3233","author":[{"given":"Wentao","family":"Gao","sequence":"first","affiliation":[{"name":"University of South Australia, Adelaide, SA, Australia"}]},{"given":"Jiuyong","family":"Li","sequence":"additional","affiliation":[{"name":"University of South Australia, Adelaide, SA, Australia"}]},{"given":"Lin","family":"Liu","sequence":"additional","affiliation":[{"name":"University of South Australia, Adelaide, SA, Australia"}]},{"given":"Thuc Duy","family":"Le","sequence":"additional","affiliation":[{"name":"University of South Australia, Adelaide, SA, Australia"}]},{"given":"Xiongren","family":"Chen","sequence":"additional","affiliation":[{"name":"University of South Australia, Adelaide, SA, Australia"}]},{"given":"Xiaojing","family":"Du","sequence":"additional","affiliation":[{"name":"University of South Australia, Adelaide, SA, Australia"}]},{"given":"Jixue","family":"Liu","sequence":"additional","affiliation":[{"name":"University of South Australia, Adelaide, SA, Australia"}]},{"given":"Yanchang","family":"Zhao","sequence":"additional","affiliation":[{"name":"CSIRO Data61, Canberra, Australia"}]},{"given":"Yun","family":"Chen","sequence":"additional","affiliation":[{"name":"CSIRO Environment, Canberra, Australia"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA250921","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:46:05Z","timestamp":1761126365000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA250921"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia250921","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}