Comparison of ice-affected streamflow estimates computed using artificial neural networks and multiple regression techniques

Abstract

The purpose of this study is to test and compare artificial neural network (ANN) and regression models for estimating river streamflow affected by ice conditions. Three regression models are investigated including: multiple regression, stepwise regression and ridge regression. A case study conducted on the Fraser River in British Columbia (Canada) is presented in which various combinations of hydrological and meteorological explanatory variables were used. Discharge estimates obtained by statistical modeling were also compared to the official estimates made by Water Survey of Canada (WSC) hydrometric technologists. The case study shows that ANN models are relatively more successful than regression models for winter streamflow estimation purposes. However, due to data scarcity, it was difficult to make a definitive assessment. Stepwise regression was found to be the most effective of the three regressive approaches investigated. Statistical modeling is a viable approach for winter streamflow data estimation, but data completeness and reliability is a major limitation.