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Utilizing Deep Learning Models to Predict Streamflow
Habtamu Alemu Workneh
,
Manoj K. Jha
Civil, Architectural and Environmental Engineering
North Carolina Agricultural and Technical State University
Industrial and systems engineering with North Carolina A&T State University
Research output
:
Contribution to journal
›
Article
›
peer-review
14
Scopus citations
Overview
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Engineering
Deep Learning Method
100%
Dimensionality
100%
Convolutional Neural Network
100%
Long Short-Term Memory
100%
Long Short-Term Memory Network
100%
Network Model
50%
Temporal Resolution
50%
Root Mean Square Error
50%
Stationarity
50%
Mean Absolute Error
50%
Principal Component
50%
Component Analysis
50%
Autocorrelation Function
50%
Dimensionality Reduction Technique
50%
Including Mean
50%
United States of America
50%
Gated Recurrent Unit
50%
Psychology
Learning Model
100%
Dimensionality Reduction
100%
Neural Network
66%
Short-Term Memory
66%
Network Model
33%
United States of America
33%
Principal Component Analysis
33%
Chemical Engineering
Deep Learning Method
100%
Long Short-Term Memory
100%
Neural Network
100%
Long Short-Term Memory Network
100%
Earth and Planetary Sciences
Streamflow
100%
Deep Learning Model
100%
North Carolina
25%
United States of America
25%
Principal Component Analysis
25%
Root-Mean-Square Error
25%
Autocorrelation
25%
Gated Recurrent Unit
25%
Bidirectional Long Short-Term Memory
25%