A seasonal ARIMA model based on the gravitational search algorithm (GSA) for runoff prediction

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Abstract

The prediction of river runoff is crucial for flood forecasting, agricultural irrigation and hydroelectric power generation. A coupled runoff prediction model based on the Gravitational Search Algorithm (GSA) and the Seasonal Autoregressive Integrated Moving Average (SARIMA) model is proposed to address the non-linear and seasonal features of runoff data. The GSA has a significant local optimisation capability, while the SARIMA model allows for real-time adjustment of the model using historical data and is suitable for analysing time series with seasonal variations. Consequently, the GSA-SARIMA model was developed and applied to the runoff prediction of the Xianyang section of the Wei River. The results suggest that the GSA-SARIMA model achieves a linear correlation coefficient of 0.9351, a Nash efficiency coefficient of 0.91, a mean relative error of 6.57 and a root mean square error of 0.21. All of the evaluation indicators of this model outperform the other models developed, and its application to actual runoff prediction is feasible, which creates a new path for runoff prediction.

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Zhang, X., Wu, X., Zhu, G., Lu, X., & Wang, K. (2022). A seasonal ARIMA model based on the gravitational search algorithm (GSA) for runoff prediction. Water Supply, 22(8), 6959–6977. https://doi.org/10.2166/ws.2022.263

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