Modelling urban air quality using artificial neural network
Authors:
- 19 February 2005
- journal article
- Published by Springer Nature in Clean Technologies and Environmental Policy
- Vol. 7 (2) , 116-126
Abstract
No abstract availableKeywords
This publication has 18 references indexed in Scilit:
- Extensive evaluation of neural network models for the prediction of NO2 and PM10 concentrations, compared with a deterministic modelling system and measurements in central HelsinkiAtmospheric Environment, 2003
- Using Improved Neural Network Model to Analyze RSP, NOx and NO2 Levels in Urban Air in Mong Kok, Hong KongEnvironmental Monitoring and Assessment, 2003
- Neural networks and periodic components used in air quality forecastingAtmospheric Environment, 2001
- Statistical surface ozone models: an improved methodology to account for non-linear behaviourAtmospheric Environment, 2000
- Neural network modelling and prediction of hourly NOx and NO2 concentrations in urban air in LondonAtmospheric Environment, 1999
- Performance evaluation of general finite line source model for Delhi traffic conditionsTransportation Research Part D: Transport and Environment, 1999
- Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciencesAtmospheric Environment, 1998
- Comparing Neural Networks and Regression Models for Ozone ForecastingJournal of the Air & Waste Management Association, 1997
- Neural Networks in Civil Engineering. I: Principles and UnderstandingJournal of Computing in Civil Engineering, 1994
- A neural network-based method for short-term predictions of ambient SO2 concentrations in highly polluted industrial areas of complex terrainAtmospheric Environment. Part B. Urban Atmosphere, 1993