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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/18673
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dc.contributor.authorMeena P.K.-
dc.contributor.authorHardaha M.K.-
dc.contributor.authorKhare, Deepak-
dc.contributor.authorMondal A.-
dc.contributor.editorDeep K.-
dc.contributor.editorNagar A.-
dc.contributor.editorPant M.-
dc.contributor.editorBansal J.C.-
dc.date.accessioned2020-12-03T04:07:01Z-
dc.date.available2020-12-03T04:07:01Z-
dc.date.issued2014-
dc.identifier.citationProceedings of Advances in Intelligent Systems and Computing, (2014), 237- 246-
dc.identifier.isbn9.79E+12-
dc.identifier.issn21945357-
dc.identifier.urihttps://doi.org/10.1007/978-81-322-1768-8_22-
dc.identifier.urihttp://repository.iitr.ac.in/handle/123456789/18673-
dc.description.abstractIn the recent years, a variety of mathematical models relating to crop yield have been proposed. A study on Neural Method for Site –Specific Yield Prediction was undertaken for Jabalpur district using Artificial Neural Networks (ANN). The input dataset for crop yield modeling includes weekly rainfall, maximum and minimum temperature and relative humidity (morning, evening) from 1969 to 2010. ANN models were developed in Neural Network Module of MATLAB (7.6 versions, 2008). Model performance has been evaluated in terms of MSE, RMSE and MAE. The basic ANN architecture was optimized in terms of training algorithm, number of neurons in the hidden layer, input variables for training of the model. Twelve algorithms for training the neural network have been evaluated. Performance of the model was evaluated with number of neurons varied from 1 to 25 in the hidden layer. A good correlation was observed between predicted and observed yield (r = 0.898 and 0.648). © Springer India 2014.-
dc.language.isoen_US-
dc.publisherSpringer Verlag-
dc.relation.ispartofProceedings of Advances in Intelligent Systems and Computing-
dc.subjectArtificial neural network-
dc.subjectCrop yield-
dc.subjectSite specific-
dc.titleNeural method for site-specific yield prediction-
dc.typeConference Paper-
dc.scopusid56513694100-
dc.scopusid56046268400-
dc.scopusid14060295600-
dc.scopusid56185631400-
dc.affiliationMeena, P.K., Department of Water Resources and Management, IIT, Roorkee, India-
dc.affiliationHardaha, M.K., Department of Soil and Water Engineering, JNKVV, Jabalpur, India-
dc.affiliationKhare, D., Department of Water Resources and Management, IIT, Roorkee, India-
dc.affiliationMondal, A., Department of Water Resources and Management, IIT, Roorkee, India-
dc.description.correspondingauthorMeena, P.K.; Department of Water Resources and Management, IITIndia; email: pramodcae@gmail.com-
dc.identifier.conferencedetails3rd International Conference on Soft Computing for Problem Solving, SocProS 2013, 26-28 December 2013-
Appears in Collections:Conference Publications [WR]

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