Forecasting hourly PM10 concentration in Cyprus through artificial neural networks and multiple regression models: implications to local environmental management

Abstract

In the present work, two types of artificial neural network (NN) models using the multilayer perceptron (MLP) and the radial basis function (RBF) techniques, as well as a model based on principal component regression analysis (PCRA), are employed to forecast hourly PM(10) concentrations in four urban areas (Larnaca, Limassol, Nicosia and Paphos) in Cyprus. The model development is based on a variety of meteorological and pollutant parameters corresponding to the 2-year period between July 2006 and June 2008, and the model evaluation is achieved through the use of a series of well-established evaluation instruments and methodologies. The evaluation reveals that the MLP NN models display the best forecasting performance with R (2) values ranging between 0.65 and 0.76, whereas the RBF NNs and the PCRA models reveal a rather weak performance with R (2) values between 0.37-0.43 and 0.33-0.38, respectively. The derived MLP models are also used to forecast Saharan dust episodes with remarkable success (probability of detection ranging between 0.68 and 0.71). On the whole, the analysis shows that the models introduced here could provide local authorities with reliable and precise predictions and alarms about air quality if used on an operational basis.

Hybridization of Air Quality Forecasting Models Using Machine Learning and Clustering: An Original Approach to Detect Pollutant Peaks

“…Some authors use boosting, that is to say increase the frequency of such episodes in the training set (Kukkonen et al, 2003;Paschalidou et al, 2010), but it can lead to overfitting. Another way to improve the precision for high concentration is to build a forecasting model with the time series of maximum daily values of the pollutant as target (Corani, 2005;Lu et al, 2006;Perez, 2012); but working with daily values does not bring information on air quality evolution during the day, which is useful for operational use.…”

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

“…The Elman network has a feedback structure (Elman, 1990) and has proven to perform well when modeling complex processes related to pollution prediction (Brunelli et al, 2007). All three networks have demonstrated good performance when modeling complex processes related to air pollution formation (Brunelli et al, 2007;Osowski and Garanty, 2007;Paschalidou et al, 2011).…”

“…Cai et al (2009) presented methods in forecasting hourly air pollutant concentrations in Guangzhou, China, using a backpropagation NN. Paschalidou et al (2011) used MLP and radial basis function NN, as well as a principal component regression analysis to make reliable forecasting of hourly PM 10 concentrations in Cyprus. Wu et al (2011) considered dust storms when improving the Elman network in predicting PM 10 API in Wuhan, China.…”

Prediction of Particulate Matter Concentrations Using Artificial Neural Network

“…The forecasting of the airborne particulate matter concentrations is of particular interest due to its well known adverse health impact to humans [2]. In the previous study, multiple regressions analysis of data was carried out to develop the statistical equations for the prediction of PM 10 and TSP [1].…”

“…regression models). This is because they have the better adaptation ability on fitting data to describe highly nonlinear physical processes [2]. The artificial neural network models has been used to predict different air pollutants like atmospheric sulphur dioxide, nitrogen oxides and particulate matter [3][4][5].…”