Better prediction of humidity using Artificial Neural Network

Prediction of humidity is one important and challenging task that needs lot of attention and study for analyzing atmospheric conditions, specially the warm weather. Advent of digital computers and development of data driven artificial intelligence approaches like Artificial Neural Networks (ANN) have helped in numerical prediction of humidity. However, very few works have been done till now in this area. The present study developed an ANN model based on the past observations of several meteorological parameters like temperature, humidity, air pressure and vapour pressure as an input for training the model. The novel architecture of the proposed model contains several multilayer perceptron network (MLP) to realize better performance. The model is enriched by analysis of several alternative models like online feature selection MLP (FSMLP) and self organizing feature map MLP (SOFM-MLP). The improvement of the performance in the prediction accuracy has been demonstrated by the selection of the appropriate features. The FSMLP is used as preprocessor to select good features. The results obtained after applying the model indicate that it is well suitable for humidity as well as warm weather prediction over large geographical areas.