Abstract: Abstract： Due to the strong time series non-stationarity and complexity in historical data of grain production， the traditional single Extreme Learning Machine （ELM） models suffer from low prediction accuracy and poor robustness. This paper optimizes the internal parameters of Whale Optimization Algorithm （WOA） and superimpose the predicted results of decomposed components model to achieve more accurate predictions of grain production. Firstly， the Empirical Mode Decomposition （EMD） model is introduced to extract intrinsic features from raw data before establishing the prediction model. Secondly， the multiple stationary grain mode components are obtained by decomposition， and a prediction model is established for each component. The experimental results show that the proposed EMD-ELM-WOA combined prediction model outperforms single ELM neural network， BP neural network， SVM model， and EMD-ELM model with minimal prediction error and highest accuracy.

Key words: Key words： empirical mode decomposition, extreme learning machine, grain production prediction, signal processing, feature extraction