C4.5: programs for machine learning
C4.5: programs for machine learning
The Development of the AQ20 Learning System and Initial Experiments
Proceedings of the International Symposium on "Intelligent Information Systems X"
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
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In this article an approach is presented where machine learning classifiers are used to drive an ensemble modeling method of multiple atmospheric transport and dispersion simulations. The goal is to achieve a higher spread of the results with a lower number of ensemble simulations. Symbolic machine learning algorithms are used to define choices for the variation of meteorological input data, model parameters, model physics, based on their combined effects on the final dispersion calculations (i.e., construction of ensembles). The methodology uses an iterative approach with the aim to identify ensemble members leading to a more balanced distribution of results. The methodology is tested using real meteorological data from Istanbul, Turkey, simulating atmospheric releases along the Bosphorus channel. In an extensive evaluation, different settings of the approach are compared in a series of experiments. The results indicate that the desired effect of more balanced results of the ensemble members can be achieved by the approach.