Year: 2018
Type: article
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.
Language: English
Subfield: Water Science and Technology
Field: Environmental Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: diamond
OpenAlex ID: W4237364183