Abstract
The regional ecological risk model is built to predict the regional ecological risk level more accurately by using principal component analysis and optimizing standard BP neural network. Taking Xiangxi Tujia and Miao Autonomous Prefecture as an example, twelve primary factors affecting regional risk are selected. The sample data are processed by principal component analysis. The obtained main components are then used as input factors of the improved BP neural network, and the level of ecological risk is used as output factor. The results indicate that the error between the expected output and the actual output is 4.36% in 2016, 1.08% in 2017, and 5.18% in 2018, respectively, with all controlled within 6%. Compared with the prediction accuracy made by standard BP neural network without principal component analysis, the prediction accuracy made by improved BP neural network with principal component analysis is greatly improved. This comprehensive prediction model provides a better evaluation method for prediction of ecological risk level.
Highlights
Just like political security, economic security, and military security, regional ecological security constitutes an important part of national security
Due to the excessive input data, the efficiency of standard BP neural network method is obviously decreased [7]. In view of these disadvantages, to predict the regional ecological risk level more accurately, a model which combines the principal component analysis method with improved BP neural network method is built in this paper. e principal components of the original sample data are analyzed by the SPSS software. ese independent principal components can summarize most of the information of the raw data and can be used as the input factors for the improved BP neural network
From 2016 to 2018, the ecological risk levels of Xiangxi Tujia and Miao Autonomous Prefecture are the levels of III,III, and IV. e relative error between the actual output and the desired output brought by improved BP neural network with Principal component analysis (PCA) is less than 6%; the relative error brought by standard BP neural network without PCA is greater than 9%
Summary
Economic security, and military security, regional ecological security constitutes an important part of national security. Due to the excessive input data, the efficiency of standard BP neural network method is obviously decreased [7] In view of these disadvantages, to predict the regional ecological risk level more accurately, a model which combines the principal component analysis method with improved BP neural network method is built in this paper. Ese independent principal components can summarize most of the information of the raw data and can be used as the input factors for the improved BP neural network In this way, the efficiency of this model can be greatly improved, which increases the prediction accuracy of regional ecological risk
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