Research on lake water level dynamic adjustment model based on ARIMA and pPSA


  • Xinyi Wu
  • Huaizheng Lu
  • Kexin Huang
  • Ruoyu Wang



Difference equations, pPSA, water level adjustment, sensitivity analysis Introductio.


The aim of this study is to construct a time-water level dynamic adjustment model to effectively manage lake levels and maximize the needs of relevant stakeholders. First, the time-series information was stabilized by constructing difference equations and using lagged differences, thereby eliminating data fluctuations. Second, by fuzzy modeling natural factors (e.g., evaporation and precipitation), we constructed an autoregressive integral moving average model (ARIMA) for fuzzy modeling of water levels. Finally, we use perturbed particle swarm algorithm (pPSA) for dynamic adjustment of water level to avoid local optimal solutions and improve model adaptability. Experimental results show that our model has better performance in stabilizing water level, accurate prediction and sensitivity analysis.


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How to Cite

Wu, X., Lu, H., Huang, K., & Wang, R. (2024). Research on lake water level dynamic adjustment model based on ARIMA and pPSA. Highlights in Science, Engineering and Technology, 105, 264-272.