Multi-Factor Water Level Prediction Based on IndRNN-Attention

Author:

Lv Haifeng,Ning Yishuang,Ning Ke,He Sheng,Lin Hongquan

Publisher

Springer Nature Switzerland

Reference13 articles.

1. Galavi, H., Mirzaei, M., Shul, L.T., et al.: Klang River–level forecasting using ARIMA and ANFIS models. J. Am. Water Works Ass. 105(9), 496–506 (2013)

2. Guo, T., He, W., Jiang, Z., et al.: An improved LSSVM model for intelligent prediction of the daily water level. Energies 12(1), 112 (2018)

3. Ci, B.C., Zhang, P.Y.: Financial time series forecasting based on ARIMALSTM model. Stat. Decis. 38(11), 145–149 (2022)

4. Sabzi, H.Z., King, J.P., Abudu, S.: Developing an intelligent expert system for streamflow prediction, integrated in a dynamic decision support system for managing multiple reservoirs: a case study. Expert Syst. Appl. 83, 145–163 (2017)

5. Yu, Z.: Research on channel water level prediction based on time series analysis. China Water Transport (Month) 18(10), 148–150 (2018)

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