Affiliations: [a] The Post-Doctoral Research Center of Zhuhai Da Hengqin Science and Technology Development Co., Ltd, China
| [b] Institute of Data Science, City University of Macau, China
| [c] Zhuhai Da Hengqin Science and Technology Development Co., Ltd, Hengqin New Area, China
Correspondence:
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Corresponding author. Deng Lianbing, Zhuhai Da Hengqin Science and Technology Development Co., Ltd. Hengqin New Area, China. E-mail: [email protected] and Cai Zhiming, Institute of Data Science, City University of Macau, China. E-mail: [email protected].
Abstract: The sponge index is the core of the sponge city flood forecast. Whether the model is reasonable or not directly affects the final forecast result. The study of classification problems using neural network models is an important branch of the artificial neural network application field. The classification and pattern recognition functions can be used to achieve flood classification and sponge index monitoring. In this paper, the author analyze the evaluation method of sponge city potential based on neural network and fuzzy mathematical evaluation. After training, the BP neural network model can effectively evaluate the potential of the sponge city, and based on the input of special information on rain conditions, it can analyze and calculate the flood risk level. It can be seen that this network model has a high mapping capability and can be correctly classified. Therefore, it is feasible to use BP neural network to solve the real-time classification of flood risk. The sponge city potential method and underground drainage system proposed in this paper can provide a reference for promoting sponge city construction.
Keywords: Sponge indicator, monitoring and tracking, neural network algorithm, internet of things
