Research Article
A Multi-factor Water Quality Prediction Method Based on Wavelet Transform and LSTM
@INPROCEEDINGS{10.1007/978-3-031-65123-6_10, author={Mingxia Yang and Lianghuai Tong and Aiping Xia and Kai Fang}, title={A Multi-factor Water Quality Prediction Method Based on Wavelet Transform and LSTM}, proceedings={Quality, Reliability, Security and Robustness in Heterogeneous Systems. 19th EAI International Conference, QShine 2023, Shenzhen, China, October 8 -- 9, 2023, Proceedings, Part II}, proceedings_a={QSHINE PART 2}, year={2024}, month={8}, keywords={Water quality prediction Wavelet transform LSTM Multi-factor}, doi={10.1007/978-3-031-65123-6_10} }- Mingxia Yang
Lianghuai Tong
Aiping Xia
Kai Fang
Year: 2024
A Multi-factor Water Quality Prediction Method Based on Wavelet Transform and LSTM
QSHINE PART 2
Springer
DOI: 10.1007/978-3-031-65123-6_10
Abstract
Water resources are an important natural resource for mankind. Protecting water resources is the key to maintaining human survival and development. Water quality is affected by many factors, including physical and chemical factors, so the accuracy of traditional water quality prediction methods is not yet satisfactory. In order to improve the accuracy of water quality prediction, this paper proposes a multi-factor water quality prediction method based on wavelet transform and LSTM (WT-LSTM). Firstly, we select multi-featured factors in the water quality data as inputs, then, noise reduction is performed on each original feature based on wavelet decomposition, and finally, the noise reduced data are input into LSTM for estimation. The experimental results show that the prediction performance of WT-LSTM is better than the original LSTM prediction model, and the multifactor prediction is better than the single-factor method. The final experimental coefficient of determination is 0.9650, which is higher than the comparison model.
