Evolutionary Design and Evaluation of Modeling System for Forecasting Urban Airborne Maximum Pollutant Concentrations

  • H. Niska
  • T. Hiltunen
  • A. Karppinen
  • M. Kolehmainen
Conference paper


In this paper, an integrated modeling system based on a multi-layer perceptron model is developed and evaluated for the forecasting of urban airborne maximum pollutant concentrations. In the first phase, the multi-objective genetic algorithm (MOGA) and sensitivity analysis are used in combination for identifying feasible system inputs. In the second phase, the final evaluation of the developed system is performed for the concentrations of pollutants measured at an urban air quality station in central Helsinki, Finland. This study showed that the evolutionary design of neural network inputs is an efficient tool, which can help to improve the accuracy of the model. The evaluation work itself showed that the developed modeling system is capable of producing fairly good operational forecasts.


True Positive Rate Numerical Weather Prediction Multiobjective Genetic Algorithm Success Index Input Selection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag/Wien 2005

Authors and Affiliations

  • H. Niska
    • 1
  • T. Hiltunen
    • 1
  • A. Karppinen
    • 2
  • M. Kolehmainen
    • 1
  1. 1.Department of Environmental SciencesUniversity of KuopioFinland
  2. 2.Finnish Meteorological InstituteHelsinkiFinland

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