Exact(6)
This paper presents an artificial neural network model that is able to predict ozone concentrations as a function of meteorological conditions and precursor concentrations.
In their study, Elkamel et al. (2001) used an artificial neural network model to predict ozone concentrations as a function of meteorological conditions and precursor concentrations in the SIA.
The development of ANN and multiple linear regressions (MLRs) has been applied to short-term prediction of the NO2 and NO x concentrations as a function of meteorological conditions.
Similarly, Abdul-Wahab and Al-Alawi (2002) used neural networks for ozone modeling in the lower atmosphere as a function of meteorological conditions and various air quality parameters in the Khaldiya residential district of Kuwait.
It was aimed to develop an appropriate neural network model in order to predict ozone concentrations in various temporal scales as a function of meteorological variables and air quality parameters.
We determined the effective sampling rates for our PAS using a mass transfer model that predicts uptake of PCBs by advection and diffusion as a function of meteorological parameters.
Similar(54)
Empirically derived decay functions for keff were developed as a function of important meteorological parameters such as solar flux, temperature, humidity, and cloud cover for various land uses and locations by statistically analyzing data generated from a detailed chemistry mechanism run over a wide range of (typical) atmospheric conditions.
Ambient PM2.5 was concentrated using a VACES, and the CAP concentration varied between experiments as a function of ambient PM2.5 levels and meteorological conditions [e.g., temperature and relative humidity; see Supplemental Material, Table 1 (http://dx.doi.org/10.1289/ehp.1104206)].
CAP concentrations varied between experiments as a function of ambient PM2.5 levels and meteorological conditions, including temperature and relative humidity [see Supplemental Material, Table 1 (http://dx.doi.org/10.1289/ehp.1104206)].
These hydro-meteorological time series are then fed through a conceptual rainfall-runoff model to simulate the potential changes in runoff as a function of changes in the hydro-meteorological variables, and runoff sensitivity is assessed with both correlation and Sobol' sensitivity analyses.
The difference between the factor loadings of daily pollen counts on the current and past values of daily meteorological variables as a function of the day of the year was based on only 11-year data sets.
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