首页|Extreme precipitation prediction based on neural network model - A case study for southeastern Brazil
Extreme precipitation prediction based on neural network model - A case study for southeastern Brazil
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NSTL
Elsevier
Extreme rainfall events can devastate urban and rural infrastructure, affect the economy and even lead to loss of life. In this work, we propose an approach based on Long Short Term Memory networks (LSTM) to forecast precipitation volume extreme rainfall using multivariate time series data. Our methodology combines reanalysis data from 12 isobaric pressure levels, surface data, and data from meteorological stations from Brazil's southeastern region. Our method allows handles the imbalanced data typical of time-series data used for this type of problem. In order to identify the best model, we performed several experiments with different configurations of LSTM networks. The test results showed that the best prediction model has as input previous data up to 24 h for a forecast of 6 h ahead with a mean absolute error (MAE) of 6.9 mm and root mean squared error (RMSE) of 6.94 mm. Our methodology shows the possibility to use reanalysis data from global mathematical models to obtain less computationally expensive regional models.
Extreme rain eventForecasting modelsData-driven modellingLong Short Term MemoryNeural networksMachine learningDATA-DRIVEN TECHNIQUESPERFORMANCE
Araujo, Andre de Sousa、Silva, Adma Raia、Zarate, Luis E.
NSTL
Elsevier
