Deep Transfer Learning Based on LSTM Model for Reservoir Flood Forecasting

Qiliang Zhu, Changsheng Wang, Wenchao Jin, Jianxun Ren and Xueting Yu
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Qiliang Zhu: North China University of Water Resources and Electric Power, China
Changsheng Wang: Water Conservancy and Irrigation District Engineering Construction Administration of Xixiayuan, China
Wenchao Jin: Water Conservancy and Irrigation District Engineering Construction Administration of Xixiayuan, China
Jianxun Ren: Water Resources Information Center of Henan Province, China
Xueting Yu: North China University of Water Resources and Electric Power, China

International Journal of Data Warehousing and Mining (IJDWM), 2024, vol. 20, issue 1, 1-17

Abstract: In recent years, deep learning has been widely used as an efficient prediction algorithm. However, this algorithm has strict requirements on the size of training samples. If there are not enough samples to train the network, it is difficult to achieve the desired effect. In view of the lack of training samples, this article proposes a deep learning prediction model integrating migration learning and applies it to flood forecasting. The model uses random forest algorithm to extract the flood characteristics, and then uses the transfer learning strategy to fine-tune the parameters of the model based on the model trained with similar reservoir data; and is used for the target reservoir flood prediction. Based on the calculation results, an autoregressive algorithm is used to intelligently correct the error of the prediction results. A series of experimental results show that our proposed method is significantly superior to other classical methods in prediction accuracy.

Date: 2024
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