Neuro-fuzzy and neural network systems for air quality control
Carnevale, C; Finzi, G; Pisoni, E; Volta, M
| HERO ID | 694585 |
|---|---|
| In Press | No |
| Year | 2009 |
| Title | Neuro-fuzzy and neural network systems for air quality control |
| Authors | Carnevale, C; Finzi, G; Pisoni, E; Volta, M |
| Journal | Atmospheric Environment |
| Volume | 43 |
| Issue | 31 |
| Page Numbers | 4811-4821 |
| Abstract | In order to define efficient air quality plans, Regional Authorities need suitable tools to evaluate both the impact of emission reduction strategies on pollution indexes and the costs of such emission reductions. The air quality control can be formalized as a two-objective nonlinear mathematical problem, integrating source-receptor models and the estimate of emission reduction costs. Both aspects present several complex elements. In particular the source-receptor models cannot be implemented through deterministic modelling systems, that would bring to a computationally unfeasible mathematical problem. In this paper we suggest to identify source-receptor statistical models (neural network and neuro-fuzzy) processing the simulations of a deterministic multi-phase modelling system (GAMES). The methodology has been applied to ozone and PM10 concentrations in Northern Italy. The results show that, despite a large advantage in terms of computational costs, the selected source-receptor models are able to accurately reproduce the simulation of the 3D modelling system. (C) 2008 Elsevier Ltd. All rights reserved. |
| Doi | 10.1016/j.atmosenv.2008.07.064 |
| Wosid | WOS:000270643100017 |
| Is Certified Translation | No |
| Dupe Override | No |
| Comments | Source: Web of Science WOS:000270643100017 |
| Is Public | Yes |
| Keyword | Multi-objective optimization; Particulate matter; Ozone; Source-receptor models; Neural networks; Neuro-fuzzy models |
| Is Qa | No |