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Urban Environmental Noise Pollution Prediction Model

Selection principle, in order to reduce the past to predict the impact of observation, a not select 0.05. The author in this selection were 0.05,0.30,0.60 and 0.90 respectively on four smoothing coefficient to predict the same set of data,ed hardy tøj, analysis results from the prediction accuracy can be seen that this forecast smoothing factor 0. 30 is more reasonable prediction accuracy is 97.89. Above the forecast model used after 2005 10n of environmental noise pollution within the city's forecast, the predicted results shown in Table 5.344 China Institute of Metrology of the first 17 years of the series 2006-2Ol5 number of L; 10,2015 ambient noise in the city pollution prediction results are as follows: Urban Regional Environmental Noise Pollution:. . Ⅷ === 55.11 +0.038 × 10 +1.9 × 10 A × 10 = 55.51dB (A); Urban traffic noise: l. + Lo a 69.23 +0.127 × 10 +6.9 × lO a ×】 O0 = 70.51dB (A). 4 Conclusion 1) The forecast results show that,MAC Cosmetics sale, 10n, the city's regional environmental noise pollution and traffic noise pollution was the trend of increasing year by year. Urban areas by 2015, noise pollution and urban traffic noise, respectively 55.51dB (A) and 7O. 51dB (A). And the state's fourth category of environmental noise standards, the city trunk road traffic noise limits on both sides of the region as a daytime 70dB (A). So failure to take any measures, the noise pollution would seriously endanger the lives of urban residents, and even lead to a variety of related diseases. 2) exponential smoothing used in urban environmental noise pollution prediction, smoothing factor to influence the prediction accuracy of the key factors, the need to carefully select a different smoothing system. 【References [1] Ya-hong, Wang Huaqing. GM (1,ed hardy,) the gray model in traffic noise prediction [J]. Huaqiao University (Natural Science), 1997,christian louboutin sko,18 (3) 407-409. [2] PRONEILOC. IANNEL1IF + Noisetrafficforecastingmodelscarriedoutinstandardsites1. A]. In, LSueharov. CABrebbiaUrbanTransportandtheEnvironmentforthe21STCentury [c]. uK: URBANTRANSPORTVII. 2001,535-544. [3] SHARMASAMEER, BARAISV, DIKSHITAK. Stud-iesofairqualitypredictorasedOnneuralnetworks1, J]. InternationalJournal0 {EnvironmentandPollution. 2003.19 (5) 442-453. [4] Wang Guoping. Grey System Theory in Urban Traffic Noise Prediction and Absolute Correlation analysis [J]. China Environmental Science, 1996,16 (1) :56-61. [5] Lee Gang,fred perry paris, Tao. Road traffic noise prediction model [J]. Environmental Sciences, 2002.15 (2); 56-59.1-63 Yuan Ling. Traffic noise prediction model of neural network EJ]. Chang'an University (Natural Science). 2003,23 (2) :84-87. [7] Zhang Jiping. Wu Shuo Yin. Artificial Neural Networks in road traffic noise prediction [J]. Environmental Science, 1998.18 (5); 471--477. [8] Xiaohong. Exponential smoothing method to establish the amount of urban industrial waste discharge prediction model [J]. Environmental Science. 1994.2 (3); 77-81. [9] Xue Wenping, Liu Zhaoli. Sun Yanning, et al. Value Method to surface water quality evaluation [J]. Environmental Engineering, 2003.21 (2) t67-69. [10] Guzhen Bang. Kai. Heavy Metal in Sediments Beijing Weiminghu 25O mouth since the change]. Environmental Chemistry, 2003,22 (1) :93-94. [11] Lu Shuyu, Luan wins base, Zhu Tan. Environmental Impact Assessment [M]. Beijing: Higher Education Press, 2001 {98-99. [12] Shang Jincheng, including the deposit width. Introduction to Strategic Environmental Assessment [M]. Beijing: Science Press ,3003:132-133.

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