Is Deep Better in Extreme Temperature Forecasting?

Abstract

In recent years, the application of deep learning based on artificial neural networks (ANNs) to forecast highly non-linear and complex weather phenomena, such as rainfall, wind speed, and temperature, has become an attractive pursuit in the field of environmental sciences. However, the critical research addressing the question of whether or not a deep learning network can perform better has not been completed. The current study conducted a systematic comparison of a one-hidden-layer (shallow) network and a multiple-hidden-layer (deep) network in maximum temperature forecasting Datasets of daily maximum temperature at five stations in South Korea, spanning the years 1976 to 2015, were used for training and testing the different-architecture models, respectively. With each model, one-day-ahead forecasting was made for the winter, spring, summer, and autumn seasons. Moreover, the performance and effectiveness of the models were then assessed by the root mean square error (RMSE). In addition, a genetic algorithm was applied to select the optimal network architecture. Finally, the empirical results indicated that the ANN model with one hidden layer, compared with the case of multiple-hidden-layer networks, produced the most accurate forecasts.