ANN Approach for Weather Prediction using Back Propagation
Citation
Ch.Jyosthna Devi , B.Syam Prasad Reddy , K.Vagdhan Kumar ,B.Musala Reddy ,N.Raja Nayak. "ANN Approach for Weather Prediction using Back P ropagation". International Journal of Engineering Trends and Technology(IJETT). V3(1):19-23 Jan-Feb 2012. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
Abstract
Temperature forecasting is important because they are used to protect life and property. Temperature for ecasting is the application of science and technology to predict the state of the temperature for a future time at a given location. Temperature for ecasts are made by collecting quantitative data about the current state of the atmosphere . A neural network can learn complex mappings from inputs to outputs, based solely on samples and require limited understanding from trainer, who can be guided by heuristics. In this paper , a neural network - based algorithm for predicting the temperature is presented .The Neural Networks packages upports different types of training or learning algorithms .One such algorithm is Back Propagati on Neural Network (BPN) technique. The main advantage of the BPN neural network method is that it can fairly approximate alarge class of functions . This method is more efficient than numerical differentiation. The simple meaning of this term is that our model has potential to capture the complex relationships between many factors that contribute to certain temperature. The proposed idea is tested using the real time data set. The results are compared with practical working of meteoro logical department and these results confirm that our model have the potential for successful application to temperature for ecasting.
References
[1] MohsenHayati, and Zahra Mohebi,” Application of Artificial Neural Networks for Temperature Forecasting, ” World Academy of Science, Engineering and Technology 28 2007.
[2] Arvind Sharma, Prof. Manish Manoria,” A Weather Forecasting System using concept of Soft Computing, ” pp.12 - 20 (2006)
[3] Raúl Rojas,” The back propagation algorithm of Neural Networks - A Systematic Introduction, “chapter 7, ISBN 978 - 3540605058
[4] K.M. Neaupane, S.H. Achet,” Use of backpropagation neural network for landslide monitori ng,” Engineering Geology 74 (2004) 213 – 226.
[5] http://www.wunderground.com/history/airport/VABB/2010/9/1/CustomHi st or y.html?dayend=1&monthend =9&yearend=2011&req_city=NA&req_ state=NA&req_statename=NA
[6] lai l.l . et al.” Intelligent weather forecast” Third international conference on machine learning and cybernetics, shanghai, 2004.
[7] Mathur S. et al ” A feature based neural network model for weather forecasting” World academy of science, e ngineering and technology 34 ,2007.
[8] Isa I.S. et al. “Weather forecasting using photovoltaic system and neural network” Second international conference on computational intelligence, communication systems and networks, 201 0.
[9] Baboo S.S. and Shereef I.K. ” An efficient weather forecasting system using artificial neural network” International journal
[10] Gill J. et al “Training back propagation neural networks with genetic algorithm for weather forecasting” IEEE 8th international symposium on intelligent systems and informatics serbia, 2010.
[11]” Data Mining: Concepts and Techniques ” by Jiawei Han and Mi cheline Kamber, Morgan Kaufmann , 2001
Keywords
FeedForwardNeuralNetwork,Temperature prediction,B a ck propagation,Training,ANN