Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/287063
Title: Efficient Dissemination of Weather Forecasting to Safeguard Farmers From Crop Failure Using Optimized Neural Network Model
Researcher: Bala Murali A
Guide(s): Siva Balan R.V
Keywords: Engineering and Technology,Computer Science,Computer Science Software Engineering
University: Noorul Islam Centre for Higher Education
Completed Date: 25/01/2018
Abstract: ABSTRACT newlineAccurate daily rainfall prediction is required for accurate stream flow prediction, flooding risk analysis, constructing a reliable flood control and early warning system. However, because of its nonlinearity, prediction of daily rainfall with high accuracy and long prediction lead time is difficult. In the field of weather forecasting, especially in rainfall prediction many researchers employed different data mining techniques to deal with that problem by using different predictors. There are many daily rainfall prediction methods in the literature, but they are known to yield inaccurate predictions with short lead time, require many physical parameters and involve complicated mathematical equations with huge computational burden. There is numerous method of organizing agricultural engineering substance and it remains an open research issue particularly when taking to distinctive arrangements of clients -farmers, specialists, agricultural engineers, agri-organizations-both from proficiency as well as expertise point of view. Keeping these factors and the necessities of Indian farmers in mind, this thesis is chosen to do research on efficient dissemination of rainfall forecasting to safeguard farmers from crop failure. This thesis mainly focus on incorporate developments from the perspective of database query optimization and caching, cross-lingual multimedia data storage and recovery, human computer communication and better approach for giving expert help to redress farmer s issues. In this thesis, the three concepts are explained in the different phases. The first phase is presented here is, explain the daily rainfall prediction in Pechiparai, Perunchani and Chittar region using hybridization of Particle Swarm Optimization with Neural Network. Here, the PSO algorithm is utilized to regulate the components of Radial Basis Function Neural Networks (RBF-NN). This algorithm meets the requirements of achieving minimum error to predict the rainfall. In order to increase the searching ability of PSO and minim
Pagination: 129
URI: http://hdl.handle.net/10603/287063
Appears in Departments:Department of Computer Applications

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chapter iii.pdf375.28 kBAdobe PDFView/Open
chapter ii.pdf136.06 kBAdobe PDFView/Open
chapter i.pdf189.27 kBAdobe PDFView/Open
chapter iv.pdf554.37 kBAdobe PDFView/Open
chapter vi.pdf16.02 kBAdobe PDFView/Open
chapter v.pdf6.66 MBAdobe PDFView/Open
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