Strong convective systems and the associated heavy rainfall events can trig-ger floods and landslides with severe detrimental consequences.These events have a high spatio-temporal variability,being difficult to predic...Strong convective systems and the associated heavy rainfall events can trig-ger floods and landslides with severe detrimental consequences.These events have a high spatio-temporal variability,being difficult to predict by standard meteorological numerical models.This 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 network.The recurrent part of it is a Long Short-Term Memory(LSTM)neural network.Prediction tests were performed for the city and surroundings of Campinas,located in the Southeastern Brazil.The 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 events.The attained pre-diction performance was better than that given by the persistence forecasting method for different prediction times.展开更多
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, el...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.展开更多
文摘Strong convective systems and the associated heavy rainfall events can trig-ger floods and landslides with severe detrimental consequences.These events have a high spatio-temporal variability,being difficult to predict by standard meteorological numerical models.This 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 network.The recurrent part of it is a Long Short-Term Memory(LSTM)neural network.Prediction tests were performed for the city and surroundings of Campinas,located in the Southeastern Brazil.The 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 events.The attained pre-diction performance was better than that given by the persistence forecasting method for different prediction times.
文摘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.
