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Comparison between observed and forecasted streamflow during the testing period using the HyMoLAP and LSTM models with different lead times in the Savè sub-catchment.

Domaine:

environment and energyclimate

Type de record:

dataset
Créateur:
SiaOlaEriSte
Hôte:avatar

Comparison between observed and forecasted streamflow during the testing period using the HyMoLAP and LSTM models with different lead times in the Savè sub-catchment.

Visit

figshare.com

Tags

EcologyEnvironmental Sciences not elsewhere classifiedBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedupstream flow forecastsremains challenging duemanaging water resourcesleast action principleenable uncertainty quantification+28

Licenses

CC BY 4.0

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Comparison between observed and forecasted streamflow during the testing period using the HyMoLAP and LSTM models with different lead times in the Bonou sub-catchment.

Comparison between observed and forecasted streamflow during the testing period using the HyMoLAP

Scatter plots of observed vs multi-step forecasted streamflow using HyMoLAP and the hybrid HyMoLAP–Bayesian LSTM in the Savè sub-catchment.

Scatter plots of observed vs multi-step forecasted streamflow using HyMoLAP and the hybrid HyMoLA

90% prediction intervals of the HyMoLAP-Bayesian LSTM hybrid model for uncertainty quantification during the testing period at different lead times in the Savè sub-catchment.

90% prediction intervals of the HyMoLAP-Bayesian LSTM hybrid model for uncertainty quantification

Scatter plots of observed vs. multi-step forecasted streamflow using HyMoLAP and the hybrid HyMoLAP–Bayesian LSTM in the Bonou sub-catchment.

Scatter plots of observed vs. multi-step forecasted streamflow using HyMoLAP and the hybrid HyMoL

90% prediction intervals of the HyMoLAP-Bayesian LSTM hybrid model for uncertainty quantification during the testing period at different lead times in the Bonou sub-catchment.

90% prediction intervals of the HyMoLAP-Bayesian LSTM hybrid model for uncertainty quantification