基于PCA-GA-BP模型的农村生态环境发展评价

Yongxin Wang
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摘要

在后精准扶贫时代,乡村振兴已成为全社会的共同行动,加强乡村生态环境治理,美丽乡村建设亟待推进。农业发展、农村繁荣、农民富裕,离不开良好生态环境的支撑。从生态、生产、生活和新能源四个方面构建农村生态环境发展评价指标体系,然后通过主成分分析筛选重要影响指标,在此基础上建立遗传算法和BP神经网络改进模型,得出31个省在农村生态环境发展中做出了更为严酷的选择——且后果更为严重,并给出了BP神经网络和GA-BP神经网络评价结果。结果表明:(1)总体而言,“十三五”期间,我国农村生态环境发展指数逐步改善,但变化幅度较小,平均值由0.2257上升至0.2431;发展水平优良省份由5个增加到7个,京津冀等省农村生态环境发展达到优良水平;(2)区域农村生态环境发展有增减,约四分之三的省份农村生态环境发展有所改善;(3)农村生态环境发展不平衡,差异逐渐扩大;(4)与BP神经网络相比,GA-BP神经网络收敛速度快,训练、验证和总体误差小,拟合程度高,具有较好的评价效果。研究结论可为农村生态环境发展评价与改善提供依据。
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Evaluation of rural ecological environment development based on PCA-GA-BP model
In the post-targeted poverty alleviation era, rural revitalization has become a common action of the whole society, strengthen the rural ecological environment governance, and the construction of beautiful countryside needs to be promoted urgently. Agricultural development, rural prosperity and farmers’ prosperity are inseparable from the support of a good ecological environment. From ecological, production, life and new energy four aspects of the rural ecological environment development evaluation index system, and then the principal component analysis screening important influence index, on the basis of the genetic algorithm and BP neural network improvement model, 31 provinces during much starker choices-and graver consequences-in rural ecological environment development, and the BP neural network and GA-BP neural network evaluation results. The results show that: (1) Generally speaking, during the 13th Five-Year Plan period, my country’s rural ecological environment development index has gradually improved, but the change range is small, the average value has risen from 0.2257 to 0.2431; The number of provinces with excellent development levels has risen from 5 to 7, and the development of rural ecological environment in Beijing, Tianjin and other provinces has risen to excellent; (2) The development of regional rural ecological environment has increased or decreased, and about three-quarters of the provinces have improved the development of rural ecological environment; (3) The development of rural ecological environment is uneven, and the difference gradually expands; (4) Compared with BP neural network, GA-BP neural network has fast convergence speed, small training, verification and overall errors, high fitting degree, and has a good evaluation effect. The research conclusions can provide a basis for the evaluation and improvement of rural ecological environment development.
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