Abstract
In this work we address the problem of inferring the height of atmospheric boundary layer from lidar data.
From one hand the problem to reconstruct the boundary layer dynamics is addressed using a Bayesian statistical inference method. Both parameter estimation and classification to mixed/residual layer, are studied. Probabilistic specification of the unknown variables is deduced from measurements. Hierarchical Bayesian models are adopted to relax the prior assumptions on the unknowns. Markov chain Monte Carlo (MCMC) simulations are conducted to explore the high dimensional posterior state space.
On the other hand a novel neuro-fuzzy model (Fuzzy Relational Neural Network) is used to obtain an “IF-THEN” reasoning scheme able to classify future observations. Experiments on real data are introduced.
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Ciaramella, A., Riccio, A., Angelini, F., Gobbi, G.P., Landi, T.C. (2009). Statistical and Fuzzy Approaches for Atmospheric Boundary Layer Classification. In: Serra, R., Cucchiara, R. (eds) AI*IA 2009: Emergent Perspectives in Artificial Intelligence. AI*IA 2009. Lecture Notes in Computer Science(), vol 5883. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10291-2_38
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DOI: https://doi.org/10.1007/978-3-642-10291-2_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10290-5
Online ISBN: 978-3-642-10291-2
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