Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/9568
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dc.coverage.spatialCivil Engineeringen_US
dc.date.accessioned2013-06-27T05:36:08Z-
dc.date.available2013-06-27T05:36:08Z-
dc.date.issued2013-06-27-
dc.identifier.urihttp://hdl.handle.net/10603/9568-
dc.description.abstractMany Indian reservoirs have lost their storage capacities because of sedimentation. Many of these reservoirs are losing capacity at the rate of0.2 1.0% annually. The sediment brought from upstream catchment through run off result in loss of capacity of reservoir. Thus there is an interaction between the runoff and sediment yield in a watershed. For a sustainable development through water resources planning and management, timely and accurate estimation of runoff and sediment yield through appropriate modeling are essential. This knowledge will allow estimating the probable lifespan of a reservoir and moreover to take proper measures against reservoir sedimentation. In this research, an ANN was developed and used to model the rainfall-runoff relationship in Vagai river basin. For this study, 41 years of annual rainfall records of 7 rain gauge stations located in Vaigai river basin was used. This model was developed in two phases namely training and testing phase. 70% of the available data are used in training phase remaining30% data were used for testing the model. From the result, it is clear that the predicted runoff fairly matches with the observed runoff. The results and comparative study indicate that artificial neural network method is more suitable to predict runoff when compared to analytical model. From this result, Vaigai reservoir will loose its purpose (70%loss)in the year 2108 according to analytical model whereas Vaigai reservoir will loose only 60% of its capacity as per the ANN model. Again the ANN model proves to be the best model for sediment deposition problems of reservoirs. Also, based on several performance indices, it was found that the ANN model estimated the volume of sediment retained in the reservoir with better accuracy and less effort as compared to conventional analysis. Hence, it is concluded from our research that ANN models are superior to analytical models in predicting run off and sediment yield of reservoirs.en_US
dc.format.extent-en_US
dc.languageEnglishen_US
dc.relationNo. of references 91en_US
dc.rightsuniversityen_US
dc.titleInflow and sediment yield modeling of Vaigai reservoir using analytical and neural network approachen_US
dc.title.alternative-en_US
dc.creator.researcherBaskaran Ten_US
dc.subject.keywordNeural networken_US
dc.description.noteAppendix p. 73-118, References p. 119-126en_US
dc.contributor.guideNagan Sen_US
dc.publisher.placeChennaien_US
dc.publisher.universityAnna Universityen_US
dc.publisher.institutionFaculty of Civil Engineeringen_US
dc.date.registered2010en_US
dc.date.completed2011en_US
dc.date.awarded28/07/2011en_US
dc.format.dimensions--en_US
dc.format.accompanyingmaterialNoneen_US
dc.source.universityUniversityen_US
dc.type.degreePh.D.en_US
Appears in Departments:Faculty of Civil Engineering

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01_title.pdf52.33 kBAdobe PDFView/Open
02_certificate.pdf973.97 kBAdobe PDFView/Open
03_abstract.pdf24.08 kBAdobe PDFView/Open
04_acknowledgements.pdf20.6 kBAdobe PDFView/Open
05_table of contents.pdf24.49 kBAdobe PDFView/Open
06_list of tables.pdf20.51 kBAdobe PDFView/Open
07_list of figures.pdf21.65 kBAdobe PDFView/Open
08_symbols and abbreviations.pdf42.97 kBAdobe PDFView/Open
09_chapter 1.pdf114.88 kBAdobe PDFView/Open
10_chapter 2.pdf95.73 kBAdobe PDFView/Open
11_chapter 3.pdf407.46 kBAdobe PDFView/Open
12_chapter 4.pdf105.21 kBAdobe PDFView/Open
13_chapter 5.pdf204.57 kBAdobe PDFView/Open
14_chapter 6.pdf17.19 kBAdobe PDFView/Open
15_references.pdf33.31 kBAdobe PDFView/Open
16_appendix.pdf292.78 kBAdobe PDFView/Open
17_list of papers.pdf16 kBAdobe PDFView/Open


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