Enhanced streamflow forecasting using attention-based neural network models: a comparative study in MOPEX basins

• Published in: Modeling earth systems and environment Vol. 10; no. 4; pp. 5717 - 5734
• Main Authors: Muhammad, Abdullahi Uwaisu, Muazu, Tasiu, Ying, Haihua, Ba, Abdoul Fatakhou, Tijjani, Sani, Adam, Jibril Muhammad, Bello, Aliyu Uthman, Bala, Muhammad Muhammad, Ali, Mosaad Ali Hussein, Dabai, Umar Sani, Umar Muhammad Mustapha Kumshe, Yahaya, Muhammad Sabo
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.08.2024; Springer Nature B.V
• Subjects: Artificial intelligence; Basins; Chemistry and Earth Sciences; Comparative analysis; Comparative studies; Computer Science; Data collection; Datasets; Earth and Environmental Science; Earth Sciences; Earth System Sciences; Ecosystems; Environment; Flood forecasting; Flood predictions; Flow rates; Geological surveys; Hydrologic data; Long short-term memory; Math. Appl. in Environmental Science; Mathematical Applications in the Physical Sciences; Neural networks; Original Article; Physics; Statistics for Engineering; Stream discharge; Stream flow; Streamflow forecasting; Time series; Attention mechanism; Hydrology; LSTM; Streamflow forecasting; MOPEX basins

To mitigate the adverse effects of floods, hydrologists are increasingly turning to artificial intelligence methodologies to enhance streamflow forecasting capabilities. Drawing inspiration from the efficacy of the Long Short-Term Memory (LSTM) model in capturing temporal dynamics and dependencies w...