{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T16:58:16Z","timestamp":1782233896959,"version":"3.54.5"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI-SS"],"abstract":"<jats:p>Soil moisture is a critical variable for water management, drought monitoring, and crop risk management, yet direct measurements below the surface are sparse and difficult to scale. We present a deep learning framework that forecasts root-zone soil moisture generated with a process-based agroecosystem simulation model, APSIM. Our approach forecasts multi-layer soil moisture profiles down to 1m using static site descriptors and daily meteorological forcings. Our approach unifies (i) large-scale physics-based simulation data generation over diverse counterfactual soil properties, irrigation strategies, and weather information, (ii) a controlled benchmark spanning Temporal Convolutional Networks, and Mamba-style state space models, and (iii) a multi-task training objective that predicts both absolute moisture levels and step-wise changes (deltas). The deltas formulation anchors forecasts to the last observed state and focuses learning on day-to-day process rates, improving stability across depths and forecast steps. Experiments on a large APSIM-derived dataset with 11 depth layers evaluate accuracy under a standard held-out test split, spatial generalization to unseen stations, and temporal generalization to a future year. Across architectures, delta-aware training consistently improves forecasting performance relative to direct level prediction and simple baselines, with the strongest gains appearing under distribution shift.<\/jats:p>","DOI":"10.1609\/aaaiss.v9i1.42916","type":"journal-article","created":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T16:34:09Z","timestamp":1782232449000},"page":"143-147","source":"Crossref","is-referenced-by-count":0,"title":["Science-Guided Multi-Task Deep Learning for Emulating APSIM Simulations for Root-zone Soil Moisture Forecasting"],"prefix":"10.1609","volume":"9","author":[{"given":"Tanjim Bin","family":"Faruk","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdul","family":"Matin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rupasree","family":"Dey","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrei","family":"Bachinin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shrideep","family":"Pallickara","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sangmi Lee","family":"Pallickara","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"9382","published-online":{"date-parts":[[2026,6,23]]},"container-title":["Proceedings of the AAAI Symposium Series"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI-SS\/article\/download\/42916\/50476","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI-SS\/article\/download\/42916\/50476","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T16:34:10Z","timestamp":1782232450000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI-SS\/article\/view\/42916"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,23]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,6,23]]}},"URL":"https:\/\/doi.org\/10.1609\/aaaiss.v9i1.42916","relation":{},"ISSN":["2994-4317"],"issn-type":[{"value":"2994-4317","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,23]]}}}