An integrated neural network model for PM10 forecasting

Pérez, Patricio; Reyes, Jorge Isaías Caicedo

doi:10.1016/j.atmosenv.2006.01.010

Cited by 164 publications

(63 citation statements)

References 12 publications

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“…G. Corani has analyzed the performance of neural networks and a linear model locally trained to forecast the daily average of PM10 in Milan [7]. Multilayer neural networks have been used for PM10 forecasting in Santiago since 2002 [8,9]. The results of most of these studies show that neural network models are more accurate than linear models for atmospheric particulate matter concentrations forecasting.…”

Section: Introductionmentioning

confidence: 99%

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Air quality forecasting in a large city

Pérez¹

2008

WIT Transactions on Ecology and the Environment

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We describe the different air pollution statistical forecasting models that have been used in Santiago, Chile during the fall/winter period for the last ten years. Effort has been concentrated on particulate matter PM10 for which a standard of 150 µg/m 3 for the 24 h average is currently established. Inputs to the models are concentrations measured at several monitoring stations distributed throughout the city and meteorological information in the region. Outputs are the expected maxima concentrations for the following day at the site of the same monitoring stations. Forecast values using neural network models are compared with the results obtained with linear models and persistence. Recently, a clustering algorithm has appeared as a potentially useful tool to detect high concentration episodes in advance.

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Section: Introductionmentioning

confidence: 99%

“…It was an artificial neural network model [9]. In this case, Equation (1) is a non linear function that can be schematically represented as a set of nodes connected by weights, in which an input layer contains the variables in parenthesis and the output layer contains the Y components.…”

Section: Forecasting Modelsmentioning

confidence: 99%

Air quality forecasting in a large city

Pérez¹

2008

WIT Transactions on Ecology and the Environment

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“…However, the linear regression cannot handle non-linearity in estimation of the PM 2.5 . To solve the problem of unable to handle non-linearity, which is often seen in PM 2.5 estimation, methodology like Artificial Neural Network (ANN) is often used to, for example, forecast the future PM 2.5 concentration (Perez et al 2006;Feng et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Estimation of PM<sub>2.5</sub> Concentrations over Beijing with MODIS AODs Using an Artificial Neural Network

Lyu

Dai

Zheng

et al. 2018

SOLA

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Three years of Aerosol Optical Depths (AODs) retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) and five meteorological parameters from the NCEP FNL reanalysis data, are used to generate an Artificial Neutral Network (ANN)-based nonlinear model for estimating the surface PM 2.5 concentrations over Beijing. To increase the number of both the training and forecasting samples for better training results and to guarantee the continuity and representativeness of the samples, the MODIS AODs are gridded with seasonally dependent windows sizes. The past PM 2.5 concentrations simulated by the ANN model are contrasted with the real observations for six years from 2008 to 2013. The results indicate that the ANN model can effectively simulate the surface PM 2.5 concentrations, and the mean bias, correlation coefficient, and the root mean square error between these data are −16.10, 0.73, and 55.43, respectively. This study also demonstrates that the Planetary Boundary Layer Height (PBLH) is the most important meteorological factor in constructing the ANN model. Compared to the linear regression model using only AOD, the correlation coefficient can be increased from 0.68 to 0.76 with the ANN model by using both the AOD and the PBLH data.

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“…It was reported to perform well in predicting high PM 10 concentrations in Greece. In addition, Perez and Reyes (2006) developed an integrated ANN to forecast maximum values of daily PM 10 concentrations in Santiago, Chile. Cai et al (2009) presented methods in forecasting hourly air pollutant concentrations in Guangzhou, China, using a backpropagation NN.…”

Section: Introductionmentioning

confidence: 99%

Improving Neural Network Prediction Accuracy for PM₁₀ Individual Air Quality Index Pollution Levels

Feng

et al. 2013

Environmental Engineering Science

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Fugitive dust deriving from construction sites is a serious local source of particulate matter (PM) that leads to air pollution in cities undergoing rapid urbanization in China. In spite of this fact, no study has yet been published relating to prediction of high levels of PM with diameters < 10 lm (PM 10 ) as adjudicated by the Individual Air Quality Index (IAQI) on fugitive dust from nearby construction sites. To combat this problem, the Construction Influence Index (Ci) is introduced in this article to improve forecasting models based on three neural network models (multilayer perceptron, Elman, and support vector machine) in predicting daily PM 10 IAQI one day in advance. To obtain acceptable forecasting accuracy, measured time series data were decomposed into wavelet representations and wavelet coefficients were predicted. Effectiveness of these forecasters were tested using a time series recorded between January 1, 2005, and December 31, 2011, at six monitoring stations situated within the urban area of the city of Wuhan, China. Experimental trials showed that the improved models provided low root mean square error values and mean absolute error values in comparison to the original models. In addition, these improved models resulted in higher values of coefficients of determination and AHPC (the accuracy rate of high PM 10 IAQI caused by nearby construction activity) compared to the original models when predicting high PM 10 IAQI levels attributable to fugitive dust from nearby construction sites.

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An integrated neural network model for PM10 forecasting

Cited by 164 publications

References 12 publications

Air quality forecasting in a large city

Air quality forecasting in a large city

Estimation of PM<sub>2.5</sub> Concentrations over Beijing with MODIS AODs Using an Artificial Neural Network

Improving Neural Network Prediction Accuracy for PM₁₀ Individual Air Quality Index Pollution Levels

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An integrated neural network model for PM10 forecasting

Cited by 164 publications

References 12 publications

Air quality forecasting in a large city

Air quality forecasting in a large city

Estimation of PM<sub>2.5</sub> Concentrations over Beijing with MODIS AODs Using an Artificial Neural Network

Improving Neural Network Prediction Accuracy for PM10 Individual Air Quality Index Pollution Levels

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Resources

About

Improving Neural Network Prediction Accuracy for PM₁₀ Individual Air Quality Index Pollution Levels