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Artificial neural networks in forecasting maximum and minimum relative humidity


Amanpreet Kaur, J K Sharma, Sunil Agrawal


Vol. 11  No. 5  pp. 197-199


In this paper, the application of neural networks to study the maximum and minimum relative humidity for Chandigarh city is explored. One important architecture of neural networks named Multi-Layer Perceptron (MLP) to model forecasting system is used and Back Propagation algorithm is used to train the network. The proposed network is trained with actual data of the past 10 years (2000-2010) and tested which comes from meteorological department. The results show that the maximum and minimum relative humidity can be predicted more accurately by using the artificial neural network.


Artificial neural network, Multi-layer perceptron, Back Propagation