A Rainfall Prediction Model Using Articial Neural Network

L. Shaikh¹ , K. Sawlani²

¹ Department of Electronics Engineering, K.J Somaiya college of engineering, Mumbai, India.

Correspondence should be addressed to: lubna.shaikh@somaiya.edu.

Section:Research Paper, Product Type: Journal
Vol.5 , Issue.1 , pp.24-28, Apr-2017

Online published on Apr 30, 2017

Copyright © L. Shaikh, K. Sawlani . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Back propagation algorithm is most commonly used in neural network projects because it works faster than earlier approaches to learning and for its accuracy. Back propagation is a workhorse of learning in neural network. In back-propagation algorithm, there are two facets in its learning cycle, one to generate input pattern and another one to adjust the output by changing the weights of the network. There are many applications of feed forward neural network such as weather and financial predictions, face and signature detections etc. Thispaper describes the training, testing of data sets and finding the number of hidden neurons using back propagation algorithm for better performance. In the research, rainfall prediction in the region of Mumbai has been analyzed using feed forward network. In formulating artificial neuralnetwork based predictive models three layered network has beenconstructed.

Key-Words / Index Term :
feed forward Network, artificial neural network, back propagation algorithm, multilayer artificial neural network component

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