Forecasting analysis of grain yield in Henan Province based on grey variable weight combination model
It is the key to ensure food security to scientifically and reasonably consider the application of various production resource elements in agriculture and promote the steady growth of grain output.The data of grain production and related factors in Henan Province from 2005～2021 were selected.And grey correlation model was used to extract the main influencing factors of grain production.Based on the inverse variance weighting method,a multivariate quantitative weight combination prediction model consisting of GM(1,N),Lasso regression and BP neural network was constructed.The change trend of grain output in Henan Province was fitted and predicted.The results showed that the prediction error of the variable weight combination prediction model was 0.589%,which had high prediction accuracy and stable performance.It was predicted that grain production in Henan would maintain a steady growth in 2022～2025 and reach 73 282.65 kt in 2025.
NETL
NSTL
万方数据
