Evolutionary Design and Evaluation of Modeling System for Forecasting Urban Airborne Maximum Pollutant Concentrations
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.
KeywordsTrue Positive Rate Numerical Weather Prediction Multiobjective Genetic Algorithm Success Index Input Selection
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