Year: 2024
Type: article
Abstract: To effectively extract information features of runoff time series and improve the accuracy of river runoff prediction methods. we integrated Autoformer with Long Short-Term Memory (LSTM) to develop an Autoformer-LSTM runoff prediction model. We simulated and validated the model using daily runoff, precipitation, and average water level data from January 1, 2002, to December 31, 2022, at Huayuankou Hydrological Station, a key station in the lower Yellow River Basin. And select Autoformer model, Transformer model, and Informer model for comparative prediction experiments. The results show that the Autoformer-LSTM and Autoformer models outperformed the Transformer and Informer models in predictive accuracy. Furthermore, compared to the Autoformer model, the Autoformer-LSTM model reduced Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) by 13.1%, 78.7%, and 53.9%. This prediction method can fully explore the inherent laws of hydrological time series data, effectively utilize the temporal nature of hydrological information, and improve prediction accuracy.
DOI: 10.54097/aesk0y28
Author: Feifei Yu
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4405900851