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A convolutional recurrent neural network for strong convective rainfall nowcasting using weather radar data in Southeastern Brazil

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Artificial Intelligence in Geosciences 2022年第1期3卷 8-13页

作者： Angelica N.Caseri Leonardo Bacelar Lima Santos stephan stephany CEMADEN-Estrada Dr.Altino Bondensan 500-Eug^enio de MeloS.J.CamposBrazil INPE-Av.dos Astronautas 1758-Jardim da GranjaS.J.Campos12.227-001Brazil

Strong convective systems and the associated heavy rainfall events can trig-ger floods and landslides with severe detrimental *** events have a high spatio-temporal variability,being difficult to predict by standard meteorological numerical *** work proposes the M5Images method for performing the very short-term prediction(nowcasting)of heavy convective rainfall using weather radar data by means of a convolutional recurrent neural *** recurrent part of it is a Long Short-Term Memory(LSTM)neural *** tests were performed for the city and surroundings of Campinas,located in the Southeastern *** convolutional recurrent neural network was trained using time series of rainfall rate images derived from weather radar data for a selected set of heavy rainfall *** attained pre-diction performance was better than that given by the persistence forecasting method for different prediction times.

关键词： Nowcasting Rainfall Extreme events Weather radar Deep learning

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An Innovative Approach for Attribute Reduction in Rough Set Theory

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Intelligent Information Management 2014年第5期6卷 223-239页

作者： Alex Sandro Aguiar Pessoa stephan stephany National Institute for Space Research (INPE) Sao Jose dos Campos Brazil

The Rough Sets Theory is used in data mining with emphasis on the treatment of uncertain or vague information. In the case of classification, this theory implicitly calculates reducts of the full set of attributes, eliminating those that are redundant or meaningless. Such reducts may even serve as input to other classifiers other than Rough Sets. The typical high dimensionality of current databases precludes the use of greedy methods to find optimal or suboptimal reducts in the search space and requires the use of stochastic methods. In this context, the calculation of reducts is typically performed by a genetic algorithm, but other metaheuristics have been proposed with better performance. This work proposes the innovative use of two known metaheuristics for this calculation, the Variable Neighborhood Search, the Variable Neighborhood Descent, besides a third heuristic called Decrescent Cardinality Search. The last one is a new heuristic specifically proposed for reduct calculation. Considering some databases commonly found in the literature of the area, the reducts that have been obtained present lower cardinality, i.e., a lower number of attributes.

关键词： Rough Set Theory Reducts Attribute Reduction Metaheuristics

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