Forecasting hourly PM(10) concentration in Cyprus through artificial neural networks and multiple regression models: implications to local environmental management.
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.
Paschalidou
Anastasia K
AK
Laboratory of Meteorology, Department of Physics, University of Ioannina, 451 10 Ioannina, Greece. apaschal@cc.uoi.gr
Karakitsios
Spyridon
S
Kleanthous
Savvas
S
Kassomenos
Pavlos A
PA
D000397:Air Pollution / Q000706:statistics & numerical data; D001185:Artificial Intelligence; D002947:Cities; D003535:Cyprus; D016208:Databases, Factual; D004784:Environmental Monitoring; D058735:Environmental Policy; D005544:Forecasting; D019083:Mediterranean Region; D015233:Models, Statistical; D016571:Neural Networks, Computer; D052638:Particulate Matter / Q000032:analysis*; D025341:Principal Component Analysis; D012621:Seasons; D013997:Time Factors; D014887:Weather
D052638:Particulate Matter