State estimation techniques for five-day streamflow forecasting with application to the Angat reservoir - Blanco, Jose Misael B.
State-space techniques and Kalman filtering are applied to the class of autoregressive moving average (ARMA) models for the purpose of real-time forecasting of 5-day inflows to the Angat reservoir. Unknown noise statistics are estimated using an adaptive recursive estimation algorithm. Forecasts are obtained using both state estimation and combined state-parameter estimation techniques. Combined state-parameter estimation allows the model parameters to be recursively updated and is an attempt to simulate the effects of increasing record length. The use of the multivariate ARMAX model (ARMA model with exogenous input) to take explicitly into account the effect of rainfall on the runoff process is also considered. Model performance is evaluated by comparing important statistical properties of the observed and forecasted data.