Water Temperature Prediction Using Improved Deep Learning Methods through Reptile Search Algorithm and Weighted Mean of Vectors Optimizer

Precise estimation of water temperature plays a key role in environmental impact assessment, aquatic ecosystems' management and water resources planning and management. In the current study, convolutional neural networks (CNN) and long short-term memory (LSTM) network-based deep learning models were examined to estimate daily water temperatures of the Bailong River in China. Two novel optimization algorithms, namely the reptile search algorithm (RSA) and weighted mean of vectors optimizer (INFO), were integrated with both deep learning models to enhance their prediction performance. To evaluate the prediction accuracy of the implemented models, four statistical indicators, i.e., the root mean square errors (RMSE), mean absolute errors, determination coefficient and Nash-Sutcliffe efficiency were utilized on the basis of different input combinations involving air temperature, streamflow, precipitation, sediment flows and day of the year (DOY) parameters. It was found that the LSTM-INFO model with DOY input outperformed the other competing models by considerably reducing the errors of RMSE and MAE in predicting daily water temperature.

Automated summary

In this study, deep learning models (CNN and LSTM) combined with two optimization algorithms (RSA and INFO) were used to accurately estimate the daily water temperature of the Bailong River in China. The experimental results showed that the LSTM-INFO model with DOY input had higher accuracy in predicting daily water temperature.