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Improving Short-term Daily Streamflow Forecasting Using an Autoencoder Based CNN-LSTM Model

Umar Muhammad Mustapha Kumshe, Z.M. Abdulhamid, Baba Ahmad Mala, Tasiu Muazu, Abdullahi Uwaisu Muhammad, Ousmane Sangary, Abdoul Fatakhou Ba, Sani Tijjani, Jibril Muhammad Adam, Mosaad Ali Hussein Ali, Aliyu Bello, Muhammad Muhammad Bala

2024Water Resources Management22 citationsDOI

Topics & Concepts

Mean squared errorAutoencoderMean absolute percentage errorArtificial neural networkComputer scienceConvolutional neural networkArtificial intelligenceStreamflowStatisticsMathematicsGeographyDrainage basinCartographyHydrological Forecasting Using AIHydrology and Watershed Management StudiesEnergy Load and Power Forecasting
Improving Short-term Daily Streamflow Forecasting Using an Autoencoder Based CNN-LSTM Model | Litcius