Deep Neural Network Modeling for Big Data Weather Forecasting

Abstract

The coming of the big data era brings the opportunities to greatly improve the forecasting accuracy of weather phenomena. Specifically, weather change is quite a complex process that is affected by thousands of variables. In the traditional computational intelligence models, we have to select the features from variables according to some fundamental assumptions, thus the correctness of these assumptions may crucially affect the prediction accuracy. Meanwhile, the principle of big data is to let data speaking, which means, when the volume of data is big enough, the hidden statistical disciplines in domain data will be revealed by the data set itself. Therefore, if massive volume of weather data is employed, we may be able to avoid using assumptions in the models, and we have the opportunity to improve the weather prediction accepted by learning the correlations hidden in the data. In our investigation, we employ a new computational intelligence technology called stacked Auto-Encoder to simulate hourly weather data in 30 years. This method can automatically learn the features from massive volume of data set via layer-by-layer feature granulation, and the large size of the data set can make sure that the complex deep model does avoid the overfitting problem. The experimental results demonstrate that using the new represented features in the classical model can obtain higher accuracy in time series problems.