Study on Rainfall Runoff Prediction of Basin Based on Digital Twin
Authors: 
Jun Chen, Xiaoli Li, Weiyan Liu, Zhenlong DuAuthors Info & Claims
Jun Chen, Xiaoli Li, Weiyan Liu, Zhenlong DuAuthors Info & ClaimsPages 576 - 583
Abstract
The precise prediction of rainfall runoff is of great significance to flood control in the basin. A digital twin-based prediction model combined with a long short-term memory (LSTM) network is proposed to achieve accurate prediction of rainfall runoff values. The digital twin model combines physical space and virtual space. According to the relevant hydrological data in the physical space, to obtain the runoff values through the LSTM prediction model and the historical data is updated in real time, reflecting the real-time and consistency between the physical space and the virtual space. Our research experiments are carried out in the Huaihe River basin, with 16 years of meteorological observations and runoff observations from 2001 to 2016 to train runoff prediction model, and the trained model is applied to the prediction of runoff. The prediction results are responded to in the twin basin scenario, and the meteorological characteristics and runoff changes are visualized in real time. The results also drive the risk assessment and conduct early warning response. The experiment shows that the proposed method achieves the combination of physical entities and data-driven, to effectively visualize the digital twin scenes. Therefore, compared with the traditional rainfall runoff prediction, it shows more intuitive and improve the perceptual response capability.
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February 2023
619 pages
ISBN:9781450398411
DOI:10.1145/3587716
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Published: 07 September 2023
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ICMLC 2023
ICMLC 2023: 2023 15th International Conference on Machine Learning and Computing
February 17 - 20, 2023
Zhuhai, China
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