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contributor authorVazirian, Roya
date accessioned2026-08-20T11:07:15Z
date available2026-08-20T11:07:15Z
date copyright2026/05/07
date issued2026
identifier otherJHYEFF.HEENG-6651.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4311720
description abstractAbstractArtificial intelligence, particularly deep learning, is transforming discharge forecasting to address climate challenges and enhance water management by capturing complex, nonlinear, long-term dependencies. This study provides a process-based ...
publisherAmerican Society of Civil Engineers
titleDeep Learning–Based Streamflow Forecasting in a Snowmelt-Dominated Basin: A Regime-Aware Evaluation
typeJournal Article
journal volume31
journal issue4
journal titleJournal of Hydrologic Engineering
identifier doi10.1061/JHYEFF.HEENG-6651
journal fristpage04026012-1
journal lastpage04026012-8
page8
treeJournal of Hydrologic Engineering:;2026:;Volume ( 031 ):;issue: 004
contenttypeFulltext


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