Hybrid river stage forecasting based on machine learning with empirical mode decomposition

Abstract The river stage is certainly an important indicator of how the water level fluctuates overtime. Continuous control of the water stage can help build an early warning indicator of floods along rivers and streams. Hence, forecasting river stages up to several days in advance is very important and constitutes a challenging task. Over the past few decades, the use of machine learning paradigm to investigate complex hydrological systems has gained significant importance, and forecasting river stage is one of the promising areas of investigations. Traditional in situ measurements, which are sometime restricted by the existing of several handicaps especially in terms of regular access to any points alongside the streams and rivers, can be overpassed by the use of modeling approaches. For more accurate forecasting of river stages, we suggest a new modeling framework based on machine learning. A hybrid forecasting approach was developed by combining machine learning techniques, namely random forest regression (RFR), bootstrap aggregating (Bagging), adaptive boosting (AdaBoost), and artificial neural network (ANN), with empirical mode decomposition (EMD) to provide a robust forecasting model. The singles models were first applied using only the river stage data without preprocessing, and in the following step, the data were decomposed into several intrinsic mode functions (IMF), which were then used as new input variables. According to the obtained results, the proposed models showed improved results compared to the standard RFR without EMD for which, the error performances metrics were drastically reduced, and the correlation index was increased remarkably and great changes in models’ performances have taken place. The RFREMD, BaggingEMD, and AdaBoostEMD were less accurate than the ANNEMD model, which had higher R≈0. 974, NSE≈0. 949, RMSE≈0. 330 and MAE≈0. 175 values. While the RFREMD and the BaggingEMD were relatively equal and exhibited the same accuracies higher than the AdaBoostEMD, the superiority of the ANNEMD was obvious. The proposed model shows the potential for combining signal decomposition with machine learning, which can serve as a basis for new insights into river stage forecasting.