Research on flood forecasting based on flood hydrograph generalization and random forest in Qiushui River basin, China

Abstract

Abstract At present, the use of hydrological models is the main technical approach for real-time flood forecasting. However, in semi-arid and arid areas, the use of the hydrological model is restricted by technical and data conditions. With the accumulation of hydrological data deluge, making full use of historical data and mining potential hydrological laws, causal relationships and other valuable information behind them provide new ideas for real-time flood forecasting in the study area. This paper develops a hybrid flood forecasting model that combines the flood hydrograph generalization method and random forest in the Qiushui River basin in the middle reaches of the Yellow River. The performance of this hybrid model is compared to that of the antecedent precipitation index model. For the development of these models, 23 flood events occurring from 1980 to 2010 are selected, of which 18 are used for calibration and 5 are used for validation. The results show that the hybrid model yields accurate predictions. And the comparison shows that the hybrid model performs better than the empirical model in the Qiushui River basin. Thus, this study provides a method for improving the accuracy of flood forecasting.