| Issue |
E3S Web Conf.
Volume 520, 2024
4th International Conference on Environment Resources and Energy Engineering (ICEREE 2024)
|
|
|---|---|---|
| Article Number | 01004 | |
| Number of page(s) | 5 | |
| Section | Multidimensional Research and Practice on Water Resources and Water Environment | |
| DOI | https://doi.org/10.1051/e3sconf/202452001004 | |
| Published online | 03 May 2024 | |
Enhanced Water Quality Prediction in the Yellow River Basin: Application of the SSA-RBF Model
1 School of Information Technology, Mapua University, Manila 1002, Philippines
2 School of Information Engineering, Yulin University, Yulin 719000, Shaanxi, China
a* Corresponding author’s email: mwu@mymail.mapua.edu.ph
b EBBlancaflor@mapua.edu.ph
Watershed water quality monitoring is of great significance in water environment protection and management, and this paper proposes a water quality prediction model based on RBF neural network. Aiming at the parameter optimisation of RBF water quality prediction model, we propose to apply the sparrow search algorithm to optimise the parameters of the RBF model, aiming to improve the global search ability and convergence speed of the model. The characteristics of water quality parameters in the Shaanxi section of the Yellow River Basin were analysed using the model. Comparison with the RBF prediction algorithm is made, and the SSA-RBF prediction algorithm can significantly improve the prediction performance of the RBF model.
© The Authors, published by EDP Sciences, 2024
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