Abstract
Air pollution in cities is an important problem influencing the environment, the well-being of society as well as its economy, the management of urban zones, etc. The problem is extremely difficult due to a very complex distribution of the pollution sources, the morphology of cities and the dispersion processes leading to a multivariate nature of the pollution phenomenon and to its high spatial-temporal variability at the local scale. Therefore, the task of understanding, modelling and predicting spatial-temporal patterns of air pollution in urban zones is an interesting and challenging topic having many research axes from science-based modelling to geostatistics and data mining. Recently, the application of land use regression models (LUR) for air pollution analysis and mapping in urban zones has demonstrated their efficiency. The present research deals with a new development of nonlinear LUR models based on machine learning algorithms. A special attention is paid to the Multi-Layer Perceptron and Random Forest algorithms and their abilities to model the NO2 pollutant in the urban zone of Geneva.
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Champendal, A., Kanevski, M., Huguenot, PE. (2014). Air Pollution Mapping Using Nonlinear Land Use Regression Models. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2014. ICCSA 2014. Lecture Notes in Computer Science, vol 8581. Springer, Cham. https://doi.org/10.1007/978-3-319-09150-1_50
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DOI: https://doi.org/10.1007/978-3-319-09150-1_50
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