A deep learning-based hybrid approach for multi-time-ahead streamflow prediction in an arid region of Northwest China

• Published in: Hydrology Research Vol. 55; no. 2; pp. 180 - 204
• Main Authors: Fang, Jinjie, Yang, Linshan, Wen, Xiaohu, Li, Weide, Yu, Haijiao, Zhou, Ting
• Format: Journal Article
• Language: English
• Published: London IWA Publishing 01.02.2024
• Subjects: Accuracy; Algorithms; Arid regions; Arid zones; Artificial intelligence; Artificial neural networks; convolutional neural network (cnn); Correlation; Cytotoxicity; Deep learning; Embedding; Hydrology; Information processing; Long short-term memory; long short-term memory (lstm); Lymphocytes T; Machine learning; Memory cells; Neural networks; Permafrost; Predictions; Resource management; River basins; shule river; Stream discharge; Stream flow; streamflow prediction; Time series; transformer; Transformers; Water resources; Water resources management; China

Accurate streamflow prediction is crucial for effective water resource management. However, reliable prediction remains a considerable challenge because of the highly complex, non-stationary, and non-linear processes that contribute to streamflow at various spatial and temporal scales. In this study...