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Mixing height short range forecasting through neural network modeling applied to radon and meteorological data

Pasini A;
2003
2003
Istituto sull'Inquinamento Atmosferico - IIA
Inglese
Third Conference on artificial intelligence applications to the environmental science
Sì, ma tipo non specificato
Long Beach
neural networks
radon
box model
An approach to prediction in the PBL is introduced in terms of the joint application of neural network and box models. Due to the relevance of radon progeny measurements for an estimation of the diffusive properties of the atmospheric boundary layer, with particular reference to the estimation of the mixing height via a box model using these data, they are added to the most usual meteorological observations in order to obtain a more accurate characterization of the boundary layer state. We perform short range forecasts of the radon concentration and, through the further application of the cited box model, we obtain reliable predictions of the mixing height in nocturnal stable situations. The neural model used is endowed with feed forward networks and backpropagation training, and was tested in the past on similar forecasting problems. Two strategies are adopted and compared: a time series approach using only inputs from time-delayed radon data or a synchronous pattern approach where meteorological and radon detections at a certain time t0 give in input the initial state of the system.
3
none
Pasini, A; Ameli, F; Lorè, M
273
info:eu-repo/semantics/conferenceObject
04 Contributo in convegno::04.01 Contributo in Atti di convegno
04.01 Contributo in Atti di convegno
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/79960
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