Flow discharge prediction of river Tigris using Self tuning predictors

Abstract

The paper presents development of recursive self-adaptive prediction algorithms called the self-tuning predictors using some common estimation techniques and their application to prediction of flow discharge of river Tigris at Baghdad, Iraq. Four kinds of predictors, namely, the least square predictor, the minimum variance predictor, predictor using stochastic approximation and a two stage predictor have been developed. Using available data for the river Tigris, prediction results have been obtained for average daily discharge, average monthly discharge and average yearly discharge. In each type of prediction, a number of models have been tried. The various prediction results are presented graphically and in tabular forms for comparison.