MULTI MODEL DATA FUSION FOR HYDROLOGICAL FORECASTING USING K-NEAREST NEIGHBOUR METHOD

Hydrological forecasting is one the most important issues in water resources systems which helps in dealing with the real time operation, flood and drought warning, and irrigation scheduling. Recent studies have suggested that the use of data fusion approach instead of using a single forecast approach may improve the hydrological forecast skill. This paper presents a comparative assessment of five different methods of data fusions including simple and weighted averaging; relying on the user’s experience; artificial neural networks; and error analysis, by applying them in two real case studies. Multiple linear regression, non-parametric K-nearest neighbour regression, conventional multilayer perceptron, and an artificial neural network improved for extreme value forecasting are used as individual forecasting methods at each case study. Conventional data fusing methods as well as a new proposed statistical method based on the non-parametric K- nearest neighbor model are used for hydrological forecasting. Results of data fusion approach in two contrasting case studies are thoroughly analyzed and discussed. The results demonstrate that the use of data fusion could significantly improve forecasts in comparison with the use of singe models. As a result of this study, it is concluded that time-varying combining methods which benefit from the use of real-time predictors in their fusion procedure could be more promising than others. Also, data fusion by K-NN method outperforms conventional methods by improving forecasts through decreasing the bandwidth of ensemble forecast and error of point forecast in both case studies.