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The multilayer artificial neural network is mostly used in Prediction related projects using Back Propagation Algorithm as it’s easy to use and due to its high accuracy result. There are two stages in its processing and learning cycle, the first one brings forth the input, and the other one matches the output by changing its weights. Three different ANN model proceed with Back Propagation Neural Network, Layer Recurrent Network, and Cascaded Back Propagation Network is developed, and the best training function and adaptive learning function is determined among these three models. Weather, financial Prediction, Face recognition, signature detection, and character recognition are some applications of the Feed Forward Neural Network. The objective of this paper is to search the best training function and adaptive learning function for the best result and best network with the Least Mean Square Error (MSE). The region of Patna (Bihar) has been selected to analyse the rainfall data, and using an artificial neural network with various weather indices, different characteristics of the hidden neurons in the system are used to study with the two-layer model used for training.
Keywords:
Rainfall Prediction, Patna, Back Propagation Algorithm, Layer Recurrent Network, Cascaded Back Propagation Mean square error.
Cite Article:
"Rainfall prediction using artificial neural network and sequential modelling", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.5, Issue 10, page no.1 - 9, October-2020, Available :http://www.ijrti.org/papers/IJRTI2010001.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator