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.

目前，水文模型的使用是实时洪水预报的主要技术手段。但是，在半干旱和干旱地区，水文模型的使用受到技术和数据条件的限制。随着水文数据洪水的积累，充分利用历史数据和挖掘潜在的水文规律，它们之间的因果关系和其他有价值的信息为研究区域的实时洪水预报提供了新思路。本文建立了一种混合洪水预报模型，将洪水水位图泛化方法与黄河中游秋水河流域的随机森林相结合。将该混合模型的性能与先前降水指数模型的性能进行了比较。为了开发这些模型，选择了1980年至2010年发生的23个洪灾事件，其中18个用于标定，5个用于验证。结果表明，混合模型产生了准确的预测。比较表明，在model水流域，混合模型的效果优于经验模型。因此，本研究提供了一种提高洪水预报准确性的方法。