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.
