A REAPPRAISAL OF THE KALMAN FILTERING TECHNIQUE, AS APPLIED IN RIVER FLOW FORECASTING

Some applications of the Kalman filtering technique in river flow fore casting are critically reviewed. It is argued that when the flow forec asting model is assumed to be an autoregressive moving average (ARMA) model and the corresponding flow data are considered to be free of mea surement errors, the minimum mean-square error forecasts obtained by u sing the 'conventional' Box and Jenkins-type time series forecasting m ethod are identical with those obtained by using the Kalman filtering technique. However, with the assumption of the presence of measurement errors in the river flow time series, the use of Kalman filtering tec hnique assumes relevance, but this type of application results in redu ced forecast efficiency as evaluted by the degree of matching attained , in the least-squares sense, of the forecasted flows with the measure d flows. In the absence of measurement error, referred to as the pure prediction scenario, it is demonstrated that a simpler degenerate set of Kalman filter equations results, in which the Kalman gain plays no part in the prediction, i.e. the application of the general Kalman fil ter becomes redundant.