Abstract
BP neural network (BPNN) is widely used due to its good generalization and robustness, but the model has the defect that it cannot automatically optimize the input variables. In response to this problem, this study uses the grey relational analysis method to rank the importance of input variables, obtains the key variables and the best BPNN model structure through multiple training and learning for the BPNN models, and proposes a variable optimization selection algorithm combining grey relational analysis and BP neural network. The predicted values from the metabolic GM (1, 1) model for key variables was used as input to the best BPNN model for prediction modeling, and a grey BP neural network model prediction model (GR-BPNN) was proposed. The long short-term memory neural network (LSTM), convolutional neural network (CNN), traditional BP neural network (BP), GM (1, N) model, and stepwise regression (SR) are also implemented as benchmark models to prove the superiority and applicability of the new model. Finally, the GR-BPNN forecasting model was applied to the grain yield forecast of the whole province and subregions for Henan Province. The forecasting results found that the growth rate of grain production in Henan Province slowed down and the center of gravity for grain production shifted northwards.
Highlights
Grain is an important strategic material related to the national economy and people’s livelihood and the most basic means of livelihood for the people
Li et al [11] and Zhang and Pan [12] studied the simulation ability and new data prediction ability of multiple linear regression model and BP neural network (BPNN) model, and the results showed that the BPNN model was better than the linear regression model in accuracy, stability, generalization degree, and theoretical basis
Based on the original sequence of 10 key impact factors, namely the corresponding index data from 2010 to 2019, the metabolic GM (1, 1) model is established, respectively, to predict the specific measurement values of key impact factors affecting grain yield in Henan Province during 2020–2025. e prediction results are shown in Table 4—prediction of grain yield in Henan Province based on grey BP neural network model prediction model (GR-BPNN) model
Summary
Grain is an important strategic material related to the national economy and people’s livelihood and the most basic means of livelihood for the people. Scientific analysis and prediction of grain yield are of great significance to the harmonious and stable development of society and the maintenance of national food security. As one of the important grain production areas in China, Henan Province has made great contributions to national food security. Erefore, scientific statistics of Henan Province grain yield data and reasonable prediction of its development trend is helpful to stabilize grain production and guarantee food security. Ese methods are simple and easy to realize, but they are only applicable to short-term grain yield prediction and are still insufficient in mining complex data information, which has great limitations [1]. E neural network models have proven their power in data mining and agricultural analysis, including crop type classification and yield prediction [4] LSTM, CNN, and BPNN have shown high accuracy and high timeliness when dealing with multivariate, multitemporal heterogeneous data and mining of nonlinear data [2, 3]. e neural network models have proven their power in data mining and agricultural analysis, including crop type classification and yield prediction [4]
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