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

Streamflow forecasting is vital for managing water resources, such as flood control, agriculture planning, hydropower generation, environmental management, drought management, and water quality management. Motivated by the success of artificial intelligence models for hydrological applications, this...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Water resources management Jg. 38; H. 15; S. 5973 - 5989
Hauptverfasser:	Kumshe, Umar Muhammad Mustapha, Abdulhamid, Zakariya Muhammad, Mala, Baba Ahmad, Muazu, Tasiu, Muhammad, Abdullahi Uwaisu, Sangary, Ousmane, Ba, Abdoul Fatakhou, Tijjani, Sani, Adam, Jibril Muhammad, Ali, Mosaad Ali Hussein, Bello, Aliyu Uthman, Bala, Muhammad Muhammad
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Dordrecht Springer Netherlands 01.12.2024 Springer Nature B.V
Schlagworte:	Artificial intelligence Artificial neural networks Atmospheric Sciences Civil Engineering data collection Datasets Drought Earth and Environmental Science Earth Sciences Environment Environmental management Flood control Flood forecasting Flood management Geotechnical Engineering & Applied Earth Sciences Hydroelectric power Hydroelectric power generation Hydrogeology Hydrology/Water Resources Long short-term memory Neural networks Performance evaluation Quality management Root-mean-square errors Statistical models Stream discharge Stream flow Streamflow forecasting water Water management water power Water quality Water quality management Water resources Water resources management Deep learning Hydrology LSTM Ala river Autoencoder Streamflow
ISSN:	0920-4741, 1573-1650
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!