Intermittent reservoir daily-inflow prediction using lumped and distributed data multi-linear regression models

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dc.contributor.authorMagar, Rajendra
dc.date.accessioned2014-03-21T10:24:43Z
dc.date.available2014-03-21T10:24:43Z
dc.date.issued2011-12
dc.description.abstractIn this study, multi-linear regression (MLR) approach is used to construct intermittent reservoir daily inflow forecasting system. To illustrate the applicability and effect of using lumped and distributed input data in MLR approach, Koyna river watershed in Maharashtra, India is chosen as a case study. The results are also compared with autoregressive integrated moving average (ARIMA) models. MLR attempts to model the relationship between two or more independent variables over a dependent variable by fitting a linear regression equation. The main aim of the present study is to see the consequences of development and applicability of simple models, when sufficient data length is available. Out of 47 years of daily historical rainfall and reservoir inflow data, 33 years of data is used for building the model and 14 years of data is used for validating the model. Based on the observed daily rainfall and reservoir inflow, various types of time-series, cause-effect and combined models are developed using lumped and distributed input data. Model performance was evaluated using various performance criteria and it was found that as in the present case, of well correlated input data, both lumped and distributed MLR models perform equally well. For the present case study considered, both MLR and ARIMA models performed equally sound due to availability of large dataset.en_US
dc.identifier.citation1067-1084en_US
dc.identifier.issn0253-4126
dc.identifier.urihttp://hdl.handle.net/123456789/861
dc.language.isoen_USen_US
dc.publisherIndian Academy of Scienceen_US
dc.relation.ispartofseriesJournal of Earth Science;Vol. 120
dc.subjectStaff Publication - SoETen_US
dc.subjectStaff Publication - CE
dc.titleIntermittent reservoir daily-inflow prediction using lumped and distributed data multi-linear regression modelsen_US
dc.typeArticleen_US
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