Specific humidity forecasting using recurrent Neural Network
Fang Chen, Xipeng Wang, Yi Lu Murphey, David M. Weber, Perry MacNeille · 2014
This paper presents our research in building a virtual humidity sensor using recurrent Neural Networks. Recurrent Neural Networks are promising methods for the prediction of time series because they provide feedback connections from hidden layer to its inputs and, therefore, can store temporal information learned from previous time steps. This study applies Elman Recurrent Neural Network (ERNN) to forecast the specific humidity from three weather stations. In addition, this study examines the feasibility of applying ERNN in time series forecasting by comparing it with multilayer perceptron network. The experiment results indicate that ERNN is a promising alternative to specific humidity forecasting.