Davidson Martins Moreira

A New Direction in the Atmospheric Pollutant Dispersion inside the Planetary Boundary Layer

AbstractIn this study, an analytical solution for the steady-state fractional advection–diffusion equation was obtained to simulate the atmospheric dispersion of pollutants in a vertically inhomogeneous planetary boundary layer. The authors propose a method that uses the modified generalized integral Laplace transform technique to solve the transformed problem with a fractional derivative, resulting in a more general solution. The model results were compared with the fractional Gaussian model and demonstrate that, when considering an experimental dataset under moderately unstable conditions, fractional-derivative models perform better than traditional integer-order models.

Study of the Impact of the Topology of Artificial Neural Networks for the Prediction of Meteorological Data

In this chapter, we present an analysis of the capacity of six topologies of neural networks—multilayer perceptron (MLP), complete (CP), random (RD), scale-free (SF), small world (SW), and hybrid (HY)—to perform next-step predictions of temperature and solar radiation. For this purpose, 100 networks of each complex topology, including an MLP network and a complete network, were created, and each network contained twenty-six neurons (five entries, twenty processing units, and one output). The networks were trained for 2000 epochs and used for the next-step prediction of the selected climate variables. The MLP and Complete networks were trained 100 times, and the averages were taken for comparison. The parameters of comparison were the root mean square error (RMSE), the mean time to learn (ETL), and the mean time to predict the next step after training (ETP), using 1000 h of input data obtained from INMET (National Institute of Meteorology), a station on the coast of Bahia. The set of 1000 h of data was divided into two parts: 700 h for network training and 300 h to verify the effectiveness of learning.