{"total":1,"items":[{"citing_arxiv_id":"2608.01775","ref_index":20,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Multi-Source Dynamic Graph Learning for Compound-Flood Forecasting in Managed Coastal Systems","primary_cat":"cs.LG","submitted_at":"2026-08-03T06:48:32+00:00","verdict":"CONDITIONAL","verdict_confidence":"MODERATE","novelty_score":6.0,"formal_verification":"none","one_line_summary":"An anchored forecaster that adds bounded, regime-gated corrections from a dynamic multi-station graph to a local temporal forecast improves sustained high-water plateau prediction in South Florida without harming routine accuracy.","context_count":1,"top_context_role":"background","top_context_polarity":"unclear","context_text":"AutoTimes: Autoregressive Time Series Forecasters via Large Language Models. InAdvances in Neural Information Processing Systems, Vol. 37. 122154-122184. arXiv:2402.02370 [cs.LG] https://openreview.net/forum?id=FOvZztnp1H [20] Amir Mosavi, Pinar Ozturk, and Kwok-Wing Chau. 2018. Flood Prediction Using Machine Learning Models: Literature Review.Water10, 11 (2018), 1536. doi:10.3390/w10111536 [21] Grey Nearing, Deborah Cohen, Vusumuzi Dube, Martin Gauch, Oren Gilon, Shaun Harrigan, Avinatan Hassidim, Daniel Klotz, Frederik Kratzert, Asher Metzger, Sella Nevo, Florian Pappenberger, Christel Prudhomme, Guy Shalev, Shlomo Shenzis, Tadele Yednkachw Tekalign, Dana Weitzner, and Yossi Matias. 2024. Global Prediction of Extreme Floods in Ungauged Watersheds."}],"limit":50,"offset":0}