数据驱动的粮食产能组合预测模型.

To address the problem of numerous hyperparameters, loss of long time series information and difficulty in distinguishing primary and secondary features in Long Short-Term Memory network (LSTM) for grain yield ca- pacity prediction, this paper proposes a combined data-driven grain capacity forecasting model. In the hyperparameter part, the proposed model performs hyperparameter search optimization for LSTM by introducing Dy- namic Weights and Laplacian variation of Bald Eagle Search Optimization Algorithm (WLBES), to avoid the process of manual parameter adjustment. In the prediction part, the proposed model uses Ridge Regression (RR) to correct the residuals of the prediction results to make up for the deficiency of LSTM data loss, and adds an attention mechanism to distinguish primary and secondary features by weight size to enhance the importance of features with greater relevance to grain production. The results show that the combined WLBES-LSTM-RR model decreases the root mean square error (RMSE) by 75% and 19% compared with the LSTM and WLBES-LSTM models, respective- ly, and substantially decreases the RMSE compared with other combined models of optimized LSTM. This combined model has higher prediction accuracy in grain yield capacity prediction. [ABSTRACT FROM AUTHOR]