Author(s): Wu Dan; Liu Qixing

Linked Author(s):

Keywords: Water and sediment change; Deep-learning; Water and sediment prediction; Wuding River; Kuye River

Abstract: The water environment of the Yellow River Basin is relatively complex, and the formation of runoff and sediment content is influenced by multiple factors comprehensively. It is a complex scientific problem intertwined with multiple factors and difficult to accurately express with mathematical formulas. The establishment of traditional hydrological models cannot break free from various assumptions about the simulation and generalization of real hydrological phenomena, and analyze the objective laws of these complex physical phenomena. This study utilizes deep learning (DP) technology based on big data to deeply mine data in the Yellow River water and sediment data warehouse, and further studies the machine learning modeling theory and method for runoff and sediment prediction based on deep learning framework. On the basis of a comprehensive, massive, and complex water and sediment data warehouse, research on the Yellow River water and sediment prediction technology based on a deep learning framework has been carried out. A water and sediment prediction model for typical river basins in the main sediment producing areas of the Yellow River has been constructed (taking the Kuye River and Wuding River as examples) to predict runoff and sediment, improve local data simulation processing methods, improve prediction accuracy and efficiency, and explore new methods for watershed hydrological prediction.

DOI: https://doi.org/10.64697/HIC2024_P320

Year: 2024