相关实验视频

Updated: Jan 15, 2026

Visualizing Hyporheic Flow Through Bedforms Using Dye Experiments and Simulation
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Visualizing Hyporheic Flow Through Bedforms Using Dye Experiments and Simulation

Published on: November 18, 2015

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使用HyMoLAP基于降雨-流水模型的框架,与贝叶斯神经网络集成在贝宁乌梅河流域的贝叶斯神经网络进行多步前进的流量和不确定性预测

Sianou Ezéckiel Houénafa1, Olatunji Johnson2, Erick K Ronoh3

  • 1Department of Mathematics, Pan African University Institute for Basic Sciences, Technology and Innovation, Nairobi, Kenya.

PloS one
|October 7, 2025
PubMed
概括

这项研究使用基于最小动作原理 (HyMoLAP) 的水文模型和贝叶斯长期短期记忆 (贝叶斯 LSTM) 来提高流量预测,用于不确定性量化. 综合方法为水资源管理提供可靠的多天流量预测和不确定性间隔.

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