Water temperature prediction in the Sansha Bay based on the integration of differential regression model and transportable long short-term memory network

Lai Xiaoqian; Yu Yiqi; Liang Zhongyao; Chen Huorong; Chen Nengwang

doi:10.12284/hyxb2023027

Volume 45 Issue 4

Mar. 2023

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Lai Xiaoqian，Yu Yiqi，Liang Zhongyao, et al. Water temperature prediction in the Sansha Bay based on the integration of differential regression model and transportable long short-term memory network[J]. Haiyang Xuebao，2023, 45(4)：165–178 doi: 10.12284/hyxb2023027

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Water temperature prediction in the Sansha Bay based on the integration of differential regression model and transportable long short-term memory network

doi: 10.12284/hyxb2023027

1.
State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen 361102, China
2.
Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Xiamen University, Xiamen 361102, China
3.
Fishery Resources Monitoring Center of Fujian Province, Fuzhou 350003, China

Received Date: 2022-07-06
Rev Recd Date: 2022-09-26

Available Online: 2023-04-10

Publish Date: 2023-03-31

Abstract

Abstract

Water temperature prediction is a key technology to ensure the production of coastal fisheries and environmental safety. The existing numerical models have high development costs with insufficient business applications. This study develops a prediction method of water temperature through integrating differential regression (DR) and transferable long short-term memory (TLSTM). Taking the water temperature of Xiamen Bay (source domain, with a large number of data) and Sansha Bay (target domain, with less data) as the research object, the DR model is established based on the data of monitoring water temperature and forecast temperature in the Sansha Bay, and the TLSTM model is established based on the long-term monitoring data of water temperature in the Xiamen Bay. The pure differential regression model, mixed differential regression model and TLSTM model are integrated into the DR-TLSTM model of Sansha Bay by using variable weight algorithm, and the performance of the model is evaluated, the results are compared with the LSTM model based on only a small amount of monitoring data in the Sansha Bay. The results show that: (1) the prediction accuracy of TLSTM model is better than that of LSTM model based on a small amount of data in the target domain; (2) the DR-TLSTM model has high prediction accuracy, and the root mean square error of prediction in the next 1−7 days is 0.13−0.77℃, and the root mean square error of prediction in the next 1−3 days is less than 0.4℃; (3) the DR-TLSTM model can effectively predict the sudden rise or fall trend of water temperature, and the root mean square error of predicting the sudden change point of water temperature is 0.29−1.09℃. Based on the DR-TLSTM model, the operational information service of water temperature early warning and forecast in the Sansha Bay is realized.
- water temperature prediction,
- regression model,
- LSTM model,
- transfer learning,
- variable weight integration

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References(37)

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