| Comparison of several methods in grain production prediction |
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| DOI：10.7606/j.issn.1000-7601.2010.04.49 |
| Key Words: principal component analysis neural network method GM(1,1)grey prediction model regression analysis model grain production prediction |
| Author Name | Affiliation | | YAO Zuofang | Norheast Institute of Geography and Agro ecology, Chinese Academy of Sciences，Changchun, Jilin 13002, China
Graduate University of Chinese Academy of Sciences，Beijing 100039, China | | LIU Xingtu | Norheast Institute of Geography and Agro ecology, Chinese Academy of Sciences，Changchun, Jilin 13002, China | | YANG Fei | Instiute of Geographic Science and Natural Resources, Chinese Academy of Sciences, Bejing 100101, China | | YAN Minhua | Norheast Institute of Geography and Agro ecology, Chinese Academy of Sciences，Changchun, Jilin 13002, China |
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| Abstract: |
| In this study, four predicting methods, namely principal component analysis, BP neural network
method, GM(1,1) grey prediction model and regression analysis model, were used and compared for grain production prediction. Based on agricultural data from 1978 ~ 2007 of Jilin Province，fourteen affecting factors were chosen for anal-
ysis by SPSS and Matlab tools to build four different prediction models. And these models were also used to predict the
grain production in Jilin Province，and comparison was made of the R2 of each regression line and the prediction accuracy
of different models. The studied results showed that BP neural network method performed the best with regression R2 and estimation accuracy of 0.899 and 93.67%，principal component analysis method performed moderate grain production estimation results, with R2 of 0.834 and the accuracy of 90.45%，the next is stepwise regression analysis, while grey prediction method was the poorest. |
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