Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Monte Carlo Statistical Methods
Monte Carlo Statistical Methods
Assessment of error in air quality models using dynamic time warping
Proceedings of the 1st ACM SIGSPATIAL International Workshop on Data Mining for Geoinformatics
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A Monte Carlo algorithm is iteratively run to identify candidate sources for atmospheric releases. The values of the ground measurements of concentration are synthetically generated by a benchmark simulation of a Gaussian dispersion model. At each iteration, a Gaussian reflected plume model is applied to compute the dispersion from a candidate source, and the resulting concentrations are compared with the measurements at fixed points on the ground. Iterative algorithms for detection of atmospheric release sources are based on the optimization of an error function between numerical simulations and observations. However, the definition of error between observations and simulations by an atmospheric dispersion model is not univocal. In this paper, the comparisons are made using various error functions. The characteristics of different error functions between model predictions and sensor measurements are investigated, with a statistical analysis of the results. Sensitivity to domain size and addition of random noise to the measurements are also investigated.