An Improved Long-Term Water Level Prediction Method for N-BEATS
DOI:
https://doi.org/10.62051/ijcsit.v3n2.39Keywords:
Water level prediction, Deep learning, Time series, N-BEATS, Whale optimization algorithmAbstract
The water level of hydrological observatory has important reference significance in water resources utilization, drought and flood control and water supply and drainage. Aiming at the problem of poor accuracy and difficult training in long-term water level prediction, an improved time series model, WOA-N-Beats, is proposed, and the whale optimization algorithm is introduced to solve the problem of the model falling into local optimal solutions and the difficulty of finding optimal parameters. In the water level prediction of Wulong, Yichang, and Shashi stations in the next 512 days, the experiments show that compared with RNN and LSTM methods, WOA-N-Beats has the best MAE, MSE, and RMSE, and the enhancement effect is large, which realizes a more accurate prediction.
Downloads
References
Xiao, B., Kai, B., & Yalin, J. (2019). Numerical simulation of karst groundwater and dynamic prediction based on Modflow and ARIMA model in Fengfeng mining area. Science Technology and Engineering, 19(17), 85-90. https://doi.org/10.3969/j.issn.1671-1815.2019.17.011
Guo-liang, J. I., Man, Z. H. O. U., Tao, L. I. U., Teng-teng, H. U., & Yong, D. I. N. G. (2022). Predicting Water Level for Large Reservoirs Using Recurrent Neural Network. Journal of Yangtze River Scientific Research Institute, 39(3), 80. https://doi.org/10.11988/ckyyb.20201266
Zhu, W., Tao, T., Yan, H., Yan, J., Wang, J., Li, S., & Xin, K. (2023). An optimized long short-term memory (LSTM)-based approach applied to early warning and forecasting of ponding in the urban drainage system. Hydrology and Earth System Sciences, 27(10), 2035-2050. https://doi.org/10.19853/j.zgjsps.1000-4602.2022.05.009
Oreshkin, B. N., Carpov, D., Chapados, N., & Bengio, Y. (2019). N-BEATS: Neural basis expansion analysis for interpretable time series forecasting. arXiv preprint arXiv:1905.10437. http://arxiv.org/abs/1905.10437
Oreshkin, B. N., Dudek, G., Pełka, P., & Turkina, E. (2021). N-BEATS neural network for mid-term electricity load forecasting. Applied Energy, 293, 116918. https://arxiv.org/abs/2009.11961
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778). https://doi.org/10.1109/CVPR.2016.90
Yang, J. H., Park, S., Kim, S., Cho, Y., & Yoh, J. J. (2022). Accurate real-time monitoring of fine dust using a densely connected convolutional networks with measured plasma emissions. Chemosphere, 293, 133604. https://doi.org/10.1016/j.chemosphere.2022.133604
Mirjalili, S., & Lewis, A. (2016). The whale optimization algorithm. Advances in engineering software, 95, 51-67. https://doi.org/10.1016/j.advengsoft.2016.01.008
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
