Intermittent reservoir daily-inflow prediction using lumped and distributed data multi-linear regression models |
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| Authors: | R B MAGAR V JOTHIPRAKASH |
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| Institution: | 1.Department of Civil Engineering,Indian Institute of Technology,Bombay,India |
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| Abstract: | In 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. |
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