2023
DOI: 10.5194/egusphere-egu23-5736
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A Novel Workflow for Streamflow Prediction in the Presence of Missing Gauge Observations

Abstract: <p>Streamflow predictions are a vital tool for detecting flood and drought events. Such predictions are even more critical to Sub-Saraharan African regions that are vulnerable to the increasing frequency and intensity of such events. These regions are sparsely gauged, with few available gauging stations that are often plagued with missing data due to various causes, such as harsh environmental conditions and constrained operational resources. </p> <p>This … Show more

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Cited by 2 publications
(2 citation statements)
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“…To predict, a dense neural network layer connects the cell and hidden states to output ( , Eq. 3) after the input sequence (Mbuvha et al, 2023). where represents the weight matrices of the model, is the input data, is the previous cell state, is the previous hidden state, is the new cell state, is the new hidden state, and represent the dense layer and its weight, respectively.
Figure 4. Structure of LSTM unit.
…”
Section: Methodsmentioning
confidence: 99%
“…To predict, a dense neural network layer connects the cell and hidden states to output ( , Eq. 3) after the input sequence (Mbuvha et al, 2023). where represents the weight matrices of the model, is the input data, is the previous cell state, is the previous hidden state, is the new cell state, is the new hidden state, and represent the dense layer and its weight, respectively.
Figure 4. Structure of LSTM unit.
…”
Section: Methodsmentioning
confidence: 99%
“…Abate et al (2023) also found positive results by using GloFAS hydrological reanalysis and actual evapotranspiration (AET) from Moderate Resolution Imaging Spectroradiometer (MODIS) to calibrate the SWAT model for the ungauged Kobo-Golina catchment in Ethiopia. Alternatively, hydrological reanalyses can be used to extend existing observational records (Mbuvha et al, 2022) creating a longer record on which to calibrate hydrological models. Care must be taken to ensure that good performance of the hydrological model is expected for the basin of interest and a pre-processing step may be required before application.…”
Section: Model Calibration and Trainingmentioning
confidence: 99%