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基于指数平滑法的环境噪声污染预测模型及应用
引用本文:李建华,周挺进.基于指数平滑法的环境噪声污染预测模型及应用[J].环境科学与管理,2012,37(7):50-53.
作者姓名:李建华  周挺进
作者单位:1. 福建体育职业技术学院,福建福州,350003
2. 福建师范大学环境科学与工程学院,福建福州,350007
摘    要:为了研究城市环境噪声污染的数学模型,通过对厦门市环境噪声的实际情况的分析并结合指数平滑预测模型,提出了符合当前环境噪声的分段三次指数平滑模型。计算了不同平滑系数时数学模型的精度,同时应用建立的数学模型对福州市10年内的噪声污染进行了预测。预测发现,噪声污染呈逐年减小的趋势,到2020年区域环境噪声和交通噪声污染分别达55.09 dB(A)和67.19 dB(A)。指数平滑法的应用效果与平滑系数的选取关系密切,应用时要根据预测的精度要求和预测期限的长短,适当选取平滑系数,并对预测的精度进行分析。结果表明基于该方法预测未来中期环境噪声准确率满足使用需求,并为其他环境噪声预测提供一种新的方法。

关 键 词:指数平滑法  厦门  区域环境噪声  预测模型

Forecasting Model of Environmental Noise and Its Application Based on Exponential Smoothing Method
Li Jianhua,Zhou Tingjin.Forecasting Model of Environmental Noise and Its Application Based on Exponential Smoothing Method[J].Environmental Science and Management,2012,37(7):50-53.
Authors:Li Jianhua  Zhou Tingjin
Institution:1.Fujian Sports Vocational Education and Technical College,Fuzhou 350003,China; 2.College of Environmental Science and Engineering,Fujian Normal University,Fuzhou 350007,China)
Abstract:In order to investigate the environment noise pollution of a city,a thrice exponential smooth model was put forward by combining the analysis of the environment noise in Xiamen city,which was in accordance with the current environmental noise condition in Xiamen.Accuracy analysis of the mathematical model was conducted for the different smooth coefficients,and the model was applied to forecast the noise pollution of Xiamen in the next ten years.The forecasting results showed that the regional environmental noise and the traffic noise would decrease gradually to 55.09 dB(A)and 67.19 dB(A)in 2020,respectively.The smooth coefficient was the key factor in the application of the exponential smooth method,which should be properly selected according to the accuracy requirement and the forecasting period.The exponential smooth model could meet the demands for predicting the future environmental noise,which provided a new method for the environmental noise prediction.
Keywords:exponential smooth method  Xiamen  regional environmental noise  forecast model
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