Searched for: author%3A%22Li%2C+Yifan%22
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Tian, Zhan (author), Yu, Ziwei (author), Li, Yifan (author), Ke, Q. (author), Liu, Junguo (author), Luo, Hongyan (author), Tang, Yingdong (author)
Climate change and rapid urbanization have made it difficult to predict the risk of pollution in cities under different types of rainfall. In this study, a data-driven approach to quantify the effects of rainfall characteristics on river pollution was proposed and applied in a case study of Shiyan River, Shenzhen, China. The results indicate...
journal article 2022
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Huang, Xinxing (author), Li, Yifan (author), Tian, Zhan (author), Ye, Qinghua (author), Ke, Q. (author), Fan, Dongli (author), Mao, Ganquan (author), Chen, Aifang (author), Liu, Junguo (author)
Efficient and accurate streamflow predictions are important for urban water management. Data-driven models, especially neural network (NN) models can predict streamflow fast, while the results are uncertain in some complex river systems. Physically based models can reveal the underlying physics, but it is relatively slow and computationally...
journal article 2021