基于RBF网络的城市垃圾产量预测及可视化

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Abstract

In order to predict and control the solid waste production in city for the following few years, Chinese cities, for example, the paper was determined 8factors from 18factors that have influence in solid waste production by using multivariate feature of K-neighbor mutual information. The 8factors that have influence in solid waste production was permanent resident population, regional GDP, total retail sales of consumer goods, added value of the financial industry, added value of the industry, added value of wholesale and retail industry, added value of accommodation and catering industry, and added value of the tertiary industry. The data from 2006 to 2013 was used as the training sample. And the data from 2014 to 2015 was used as the test sample. To predict and visualize the total solid waste production in all provinces and cities from 2017 to 2018, firstly, paper was established a prediction model of radial basis function (RBF) neural network based on above-mentioned 8influence factors. Second, paper was corrected the prediction model based on the mean relative error (MRE). Third, this paper was proposed two-stage radial basis prediction model. The results showed that the MRE of the two-stage radial basis prediction model proposed in this paper was 6.43%, which was equivalent to 93.57% prediction accuracy. It was therefore obvious that the prediction accuracy of this model was high, and it was capable to predict the municipal solid waste production in real life.

Key words： municipal solid waste mutual Information radial basis function network production predict visualization

Received: 16 June 2017

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	Articles by authors
	QIN Xu-jia
	PENG Jie
	XU Fei
	ZHENG Hong-bo
	ZHANG Mei-yu

Cite this article:

QIN Xu-jia,PENG Jie,XU Fei等. Prediction and visualization of municipal solid waste production based on RBF network[J]. CHINA ENVIRONMENTAL SCIENCECE, 2018, 38(2): 792-800.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2018/V38/I2/792

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