Forecasting PM10 in metropolitan areas: Efficacy of neural networks.
Deterministic photochemical air quality models are commonly used for regulatory management and planning of urban airsheds. These models are complex, computer intensive, and hence are prohibitively expensive for routine air quality predictions. Stochastic methods are becoming increasingly popular as an alternative, which relegate decision making to artificial intelligence based on Neural Networks that are made of artificial neurons or 'nodes' capable of 'learning through training' via historic data. A Neural Network was used to predict particulate matter concentration at a regulatory monitoring site in Phoenix, Arizona; its development, efficacy as a predictive tool and performance vis-à-vis a commonly used regulatory photochemical model are described in this paper. It is concluded that Neural Networks are much easier, quicker and economical to implement without compromising the accuracy of predictions. Neural Networks can be used to develop rapid air quality warning systems based on a network of automated monitoring stations.
University of Notre Dame, Civil Engineering & Geological Sciences & Environmental Fluid Dynamics Laboratories, Notre Dame, IN 46446, USA.
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HJ
Fernando
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Mammarella
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Grandoni
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Fedele
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Di Marco
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Dimitrova
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Hyde
D000393:Air Pollutants / Q000032:analysis*; D000397:Air Pollution / Q000706:statistics & numerical data*; D001272:Atmosphere / Q000737:chemistry; D002947:Cities; D004784:Environmental Monitoring / Q000379:methods*; D005544:Forecasting; D008956:Models, Chemical; D016571:Neural Networks, Computer; D010316:Particle Size; D052638:Particulate Matter / Q000032:analysis*; D013223:Statistics as Topic
D000393:Air Pollutants; D052638:Particulate Matter