Modelo de previsão sazonal de chuva para o Estado do Ceará baseado em redes neurais artificiais - Castro, Thiago Nogueira (2011)
Climatological systems are characterized by complex modeling and having low predictability. In semi-arid regions, as the Brazilian Northeast, weather forecast information are necessary for the maintenance of life and a better use of water resources. The State of Ceará, located on the north of Brazilian Northeast, is a region that suffers with drought for a long time. The Fundação Cearense de Meteorologia e Recursos Hídricos (FUNCEME), which belongs to the state government, is responsible for generating research to bring a better phenomenological understanding on the weather of the State of Ceará and thus make a better prediction on how the rainy season will be. Today the foundation makes use of numerical modeling consisting of two regional models, the Regional Spectral Model (RSM) and the Regional Modeling Atmospheric System (RAMS), nested by a downscaling technique to the large scale dynamic model ECHAM4.5, in order to do its predictions. Dynamic models are characterized by their high computational costs, large amounts of information on its input and high complexity usage. The development of forecasting models based on Artificial Neural Networks (ANN) covers various areas of knowledge showing promising results. Neural network based models are capable of reproducing different types of systems through its learning capability. In this thesis it was developed a model for predicting rain for the eight homogeneous regions of the state of Ceará that presents low computational cost and easy use. In order to achieve this development it was used an ANN base on a Neo-Fuzzy Neuron (NFN) technique. Despite being offered a new prediction model, this thesis aims to enrich the information generated by forecast models and do a better prediction on the rainy season of the State of Ceará. The proposed model was compared to the RSM model that is currently in use by FUNCEME in its predictions. In this comparison, as performance indicators, it was used: the execution time, value of the root mean square error (RMSE) and the correlation with the observed values. At the end, it is concluded that the proposed model had a better performance and was faster than the RSM dynamic model in its predictions.