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To solve the problems of low prediction accuracy, poor stability, and low calculation efficiency in runoff forecasting, this study develops an extreme learning machine (ELM) model based on improved complete ensemble empirical mode decomposition adaptive noise (CEEMDAN). The model first uses orthogonal triangular decomposition (QR) to redefine the ELM hidden layer output to construct the orthogonal triangular ELM (QR-ELM). It then introduces the online sequence mechanism (OS) into the QR-ELM model to construct online sequence OR-ELM (OS-QR-ELM), which can effectively improve the efficiency of the ELM models. Then, it uses the correlation wave extension method to extend both ends of the original signal to solve the end effect of CEEMDAN. Finally, it integrates CEEMDAN and OS-QR-ELM combined with parallel computing ideas to construct a parallel CEEMDAN-OS-QR-ELM runoff prediction method. The experimental results show that compared with the support vector regression method combined with empirical mode decomposition, the accuracy and reliability of the parallel CEEMDAN-OS-QR-ELM model was 1.09&#x0025; and 8&#x0025; higher, respectively. Compared with the ELM model, the efficiency of OS-QR-ELM was 5&#x0025;.

Runoff Prediction and Analysis Based on Improved CEEMDAN-OS-QR-ELM