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Updated: Jun 11, 2025

Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface
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Published on: June 8, 2015

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基于孟加拉国气象数据对先进的深度学习模型进行比较分析,以预测基于孟加拉国气象数据的蒸发转化

Sourov Paul1, Syeda Zehan Farzana2,3, Saikat Das2

  • 1Department of Civil Engineering, Leading University, Sylhet, Bangladesh. sourovpaul6782@gmail.com.

Environmental science and pollution research international
|October 4, 2024
PubMed
概括

在孟加拉国预测每日蒸发转化是使用新型混合CNN-GRU深度学习模型改进的. 该模型准确地估计了用有限的气象数据来估计水资源需求,其性能优于其他方法.

关键词:
深度学习是一种深度学习.混合动力模型 混合动力模型参考蒸发透气的情况.水资源和灌方式

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A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions15:30

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

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