Incorporating Empirical Orthogonal Function Analysis into Machine Learning Models for Streamflow Prediction

Machine learning (ML) models have been widely used to predict streamflow. However, limited by the high dimensionality and training difficulty, high-resolution gridded climate datasets have rarely been used to build ML-based streamflow models. In this study, we developed a general modeling framework that applied empirical orthogonal function (EOF) analysis to extract information from gridded climate datasets for building ML-based streamflow prediction models. Four classic ML methods, namely, support vector regression (SVR), multilayer perceptron (MLP), long short-term memory (LSTM) and gradient boosting regression tree (GBRT), were incorporated into the modeling framework for performance evaluation and comparison. We applied the modeling framework to the upper Heihe River Basin (UHRB) to simulate a historical 22-year period of daily streamflow. The modeling results demonstrated that EOF analysis could extract the spatial information from the gridded climate datasets for streamflow prediction. All four selected ML models captured the temporal variations in the streamflow and reproduced the daily hydrographs. In particular, the GBRT model outperformed the other three models in terms of streamflow prediction accuracy in the testing period. The R 2 , RMSE, MAE, NSE and PBIAS were equal to 0.68, 9.40 m 3 /s, 5.18 m 3 /s, 0.68 and −0.03 for the daily streamflow in the Taolai River Watershed of the UHRB, respectively. Additionally, the LSTM method could provide physically based hydrological explanations of climate predicators in streamflow generation. Therefore, this study demonstrated the unique capability and functionality of incorporating EOF analysis into ML models for streamflow prediction, which could make better use of the readily available gridded climate data in hydrological simulations.