@Article{AnochiCamp:2016:MePrCl,
author = "Anochi, Juliana Aparecida and Campos Velho, Haroldo Fraga de",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)}",
title = "Mesoscale precipitation climate prediction for brazilian south
region by artificial neural networks",
journal = "American Journal of Environmental Engineering",
year = "2016",
volume = "6",
number = "4",
pages = "94--102",
keywords = "Climate prediction, Precipitation, Self-configured neural network,
Data reduction.",
abstract = "Numerical weather and climate use sophisticated mathematical
models. These models are employed to simulate the atmospheric
dynamics to perform a medium-range forecasting and climate
prediction. Such an approach allows to estimate all meteorological
variables for a future time period: wind fields, air temperature,
pressure, moisture, and precipitation field. Precipitation is one
of the most difficult fields for prediction. The latter statement
is verified due to high variability in space and time. However,
precipitation is a key issue in many activities of society. An
alternative approach for climate prediction to the precipitation
field is to employ the Artificial Neural Network (ANN). Such
technique has a reduced computational cost in comparison with time
integration of the partial differential equations. One challenge
to employ an ANN is to determine the topology or configuration of
a neural network. Here, a supervised ANN is designed to perform
the precipitation prediction looking at two different periods:
monthly and seasonal precipitation. The method is applied to the
Southern region of Brazil. The definition of the neural network
topology is addressed as an optimization problem. The best
configuration is computed by minimizing a cost function. The
optimization problem is solved by a new meta-heuristic:
Multi-Particle Collision Algorithm (MPCA). In addition, a
technique based on rough set theory is used to reduce the data
space dimension. The predicted precipitation is evaluated by
comparison with measured data. The prediction is also evaluated
using full and reduced input data for a neural predictive model.",
doi = "10.5923/s.ajee.201601.14",
url = "http://dx.doi.org/10.5923/s.ajee.201601.14",
issn = "2166-4633 and 2166-465X",
label = "lattes: 2720072834057575 1 AnochiCamp:2016:MePrCl",
language = "en",
url = "http://article.sapub.org/10.5923.s.ajee.201601.14.html",
urlaccessdate = "29 mar. 2024"
}