外生输入非线性自回归模型预测颗粒物浓度

IF 3.1 Q2 ENVIRONMENTAL SCIENCES
Muhammad Izzuddin Rumaling, F. Chee, Haoqian Chang, C. Payus, S. Kong, J. Dayou, J. Sentian
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引用次数: 2

摘要

背景和目的在一些发展中国家,空气质量主要是由颗粒物,特别是10微米及以下的颗粒物或PM10所控制。它们可以被吸入,有时可以深入肺部;有些甚至会进入血液,造成严重的健康问题。因此,未来PM10浓度预测对于早期预防和城市发展规划具有重要意义,对发展中城市至关重要。本文介绍了基于非线性自回归外生输入模型的PM10预测模型的发展。方法为了提高外源输入非线性自回归模型的性能,在模型前采用主成分分析进行变量选择。主成分分析的第一阶段涉及到Scree plot,它根据解释方差确定主成分的数量。然后,根据其对PM10浓度变化的贡献强度,使用旋转分量矩阵选择变量。为了验证该模型,使用了2003年至2010年哥打京那巴鲁的PM10数据。神经网络模型是利用这些数据,通过改变输入变量的数量,包括时间变量来开发的。利用2011 ~ 2012年北京市PM10数据对所建立的预测模型进行了评价。报告了四项性能指标,即均方根误差、平均绝对误差、一致性指数和分数偏差。主成分分析结果表明,风向指数、相对湿度、环境温度、二氧化氮浓度和臭氧浓度等5个变量对PM10浓度的变化有重要影响。将这些变量与时间变量一起作为非线性自回归外生输入模型的输入,所得模型具有良好的预测性能,均方根误差为7.086±0.873µg/m3。重要变量的选择有助于减少预测模型内的输入变量,而不会降低其预测性能。结论该模型所需输入变量较少,不需要进行变量转换,在预测亚打京那巴鲁市PM10浓度方面具有良好的应用前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Forecasting particulate matter concentration using nonlinear autoregression with exogenous input model
BACKGROUND AND OBJECTIVES Air quality in some developing countries is dominated by particulate matter, especially those with size 10 micrometers and smaller or PM10. They can be inhaled and sometimes can get deep into lungs; some may even get into bloodstream and cause serious health problems. Therefore, future PM10 concentration forecasting is important for early prevention and in urban development planning, which is crucial for developing cities. This paper presents the development of PM10 forecasting model using nonlinear autoregressive with exogenous input model.METHODS To improve performance of nonlinear autoregressive with exogenous input model, principal component analysis is used prior to the model for variable selection. The first stage of principal component analysis involves Scree plot, which determines the number of principal components based on explained variance. This is then followed by selecting variables using a rotated component matrix, based on their strength of contribution towards variation of PM10 concentration. To test the model, PM10 data in Kota Kinabalu from 2003 – 2010 was used. Neural network models are developed using this data by varying number of input variables with the inclusion of temporal variables. The developed forecasting models are evaluated using data PM10 in the city from 2011 to 2012. Four performance indicators, namely root mean square error, mean absolute error, index of agreement and fractional bias are reported.FINDINGS Results from principal component analysis show that five variables including wind direction index, relative humidity, ambient temperature, concentration of nitrogen dioxide and concentration of ozone strongly contribute to the variation of PM10 concentration.  By using these variables together with temporal variables as input in the nonlinear autoregressive with exogenous input models, the resultant model shows good forecasting performance, with root mean square error of 7.086±0.873 µg/m3. The selection of significant variables helps in reducing input variables inside the forecast model without degrading its forecast performance.CONCLUSION This model shows very promising performance in forecasting PM10 concentration in Kota Kinabalu as it requires fewer input variables and does not require variable transformation.
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来源期刊
CiteScore
7.90
自引率
2.90%
发文量
11
审稿时长
8 weeks
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