基于BP神经网络的多因素城市生活垃圾产量预测模型研究

运用BP神经网络理论,通过对城市生活垃圾MSW与人口数、GDP、人均收入和人均消费的数据分析,研究建立了多因素BP神经网络模型,并以北京市城市生活垃圾数据进行应用,得到了较好的效果。该模型优于传统灰色理论模型及双因素BP神经网络模型,预测结果合理,精度高,实用性强。该研究方法对城市生活垃圾管理及环境规划具有一定的指导意义,对其他领域一些相关问题也具有实用参考价值。

Study on the Multi-factor Yield Prediction Model of Municipal Solid Waste Based on BP Neural Network

Using BP neural network theory, the data of municipal solid waste(MSW), population, GDP, per capita income, per capita consumption were analyzed to set up the multi-factor BP neural network model. Applying the data of municipal solid waste in Beijing, better effects were obtained. The new model is superior to gray theory modeling and two-factor BP neural network model for its reasonable prediction results, high accuracy and strong practicability. It is not only an instructive research to the dealing of MSW and environmental project, but it is also practically valuable to other problems in some related fields.