Research on Short-Term Multi-Step Prediction of River Dissolved Oxygen based on STL-LSTM

Bo Chen; Tuokai Cao; Lidong Yao

doi:10.54097/qtvc2x70

Authors

Bo Chen
Tuokai Cao
Lidong Yao

DOI:

https://doi.org/10.54097/qtvc2x70

Keywords:

Dissolved Oxygen Prediction, STL, Time Series, LSTM, Multi-step Predict

Abstract

In order to improve the multi-step prediction accuracy of dissolved oxygen in rivers, this paper proposes a multi-step prediction model for dissolved oxygen in rivers based on the combination of STL-LSTM and multi input multi output strategy (STL-MIMO-LSTM). Firstly, the dissolved oxygen is decomposed into trend, seasonal, and residual components using the Seasonal and Trend decomposition using Loess (STL) method to enhance data features. Then, the model is constructed using the multi-input multi-output strategy (MIMO) combined with the Long Short-Term Memory (LSTM) model; Finally, the LSTM model was applied to the prediction experiment of dissolved oxygen in the Beijing-Hangzhou Grand Canal. The experimental results show that the mean absolute error (MAE) of the STL-MOMO-LSTM model in the next 4 steps is 0.07, 0.1024, 0.1211, 0.1319, and the mean square error (MSE) is 0.0134, 0.0288, 0.0399, 0.0467. Except for the first time step, the prediction accuracy is better than the recursive and direct multi-step prediction models. The MSE prediction accuracy decay rates of STL-MIMO-LSTM for he 2nd, 3rd, and 4th time steps are 1.1496%, 0.3878%, and 0.1688%, respectively, which are lower than other models for the 2nd and 3rd-time steps, while slightly higher than the STL-DIRECT-LSTM model (0.1501%) for 4th-time steps. Therefore, the model exhibits high prediction accuracy and stable prediction performance, can effectively predict the future trend of dissolved oxygen, and provide references for river water quality management.

Downloads

Download data is not yet available.

References

Franklin, P. A. (2014). Dissolved oxygen criteria for freshwater fish in New Zealand: a revised approach. New Zealand journal of marine and freshwater research, 48(1), 112-126.

Ren Q, Wang X, Li W, et al. Research of dissolved oxygen prediction in recirculating aquaculture systems based on deep belief network[J]. Aquacultural Engineering, 2020, 90: 102085.

Li, C., Li, Z., Wu, J., Zhu, L., & Yue, J. (2018). A hybrid model for dissolved oxygen prediction in aquaculture based on multi-scale features. Information processing in agriculture, 5(1), 11-20.

Li, W., Wei, Y., An, D., Jiao, Y., & Wei, Q. (2022). LSTM-TCN: Dissolved oxygen prediction in aquaculture, based on combined model of long short-term memory network and temporal convolutional network. Environmental Science and Pollution Research, 29(26), 39545-39556.

Kisi, O., Alizamir, M., & Docheshmeh Gorgij, A. (2020). Dissolved oxygen prediction using a new ensemble method. Environmental Science and Pollution Research, 27, 9589-9603.

Liu, Y., Zhang, Q., Song, L., & Chen, Y. (2019). Attention-based recurrent neural networks for accurate short-term and long-term dissolved oxygen prediction. Computers and electronics in agriculture, 165, 104964.

Li, C., Li, Z., Zhu, L., Wu, J., & Yue, J. (2018). Dissolved oxygen time series prediction based on LSTM deep learning neural network. International Agricultural Engineering Journal, 27(2), 353-359.

Eze, E., & Ajmal, T. (2020). Dissolved oxygen forecasting in aquaculture: a hybrid model approach. Applied Sciences, 10(20), 7079.

Ziyad Sami, B. F., Latif, S. D., Ahmed, A. N., Chow, M. F., Murti, M. A., Suhendi, A., ... & El-Shafie, A. (2022). Machine learning algorithm as a sustainable tool for dissolved oxygen prediction: a case study of Feitsui Reservoir, Taiwan. Scientific Reports, 12(1), 3649.

Nair, J. P., & Vijaya, M. S. (2022). Analysing And Modelling Dissolved Oxygen Concentration Using Deep Learning Architectures. International Journal of Mechanical Engineering, 7, 12-22.

Chandra, R., Goyal, S., & Gupta, R. (2021). Evaluation of deep learning models for multi-step ahead time series prediction. IEEE Access, 9, 83105-83123.

Abdellah, A. R., Mahmood, O. A. K., Paramonov, A., & Koucheryavy, A. (2019, October). IoT traffic prediction using multi-step ahead prediction with neural network. In 2019 11th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT) (pp. 1-4). IEEE.

Zhang, L., He, Y., Wu, H., Yang, X., & Ding, M. (2023). Ultra-short-term multi-step probability interval prediction of photovoltaic power: A framework with time-series-segment feature analysis. Solar Energy, 260, 71-82.

Taieb, S. B., Bontempi, G., Atiya, A. F., & Sorjamaa, A. (2012). A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition. Expert systems with applications, 39(8), 7067-7083.

Qin, L., Li, W., & Li, S. (2019). Effective passenger flow forecasting using STL and ESN based on two improvement strategies. Neurocomputing, 356, 244-256.

Selvin, S., Vinayakumar, R., Gopalakrishnan, E. A., Menon, V. K., & Soman, K. P. (2017, September). Stock price prediction using LSTM, RNN and CNN-sliding window model. In 2017 international conference on advances in computing, communications and informatics (icacci) (pp. 1643-1647). IEEE.

Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.