A transfer learning-based long short-term memory model for the prediction of river water temperature.

Water temperature affects many physical, chemical and biological processes in rivers and plays a crucial role in determining the quality of aquatic ecosystems. Due to the complexity and nonlinear characteristics of most factors affecting river water temperature, it is difficult for traditional models to accurately predict river water temperature. In this context, accurate prediction of river water temperature calls for new and innovative machine learning techniques. This paper presents a new hybrid model, called LSTM-Encoder, that combines long short-term memory (LSTM) with transformer encoder. To improve the accuracy of the hybrid LSTM-Encoder model, wavelet threshold denoising (WTD) method was used to denoise the data. The proposed model was compared with the Air2water model as well as eight other artificial intelligence (AI) models. The results show that the mean absolute percentage error (MAPE), mean absolute error (MAE), root mean squared error (RMSE) and coefficient of determination (R2) values of the WTD-LSTM-Encoder model are 2.9, 0.279 °C, 0.567 °C and 0.867, respectively, for the training datasets and 3.2, 0.312 °C, 0.625 °C and 0.714, respectively, for the testing datasets, indicating that the proposed WTD-LSTM-Encoder model has the best comprehensive performance in predicting the river water temperature. • A novel hybrid model coupling long short-term memory (LSTM) and transformer encoder is proposed for predicting river water temperature. • Utilization of wavelet threshold denoising (WTD) technology to reduce data noise and improve prediction accuracy. • Integration of self-attention and multi-head mechanisms enhances the prediction ability of the model. • A comparison among the proposed model, the Air2water model and eight other artificial intelligence (AI) models is conducted. [ABSTRACT FROM AUTHOR]