Three typical real-time correction methods, including the traditional error autoregressive method, the nonparametric correction method based on the K-nearest neighbor ( KNN ) , and the multi-cross section correction method based on Kalman filtering, as well as a combination of the KNN method and the Kalman filtering method were used in combination with a one-dimensional hydrodynamic model for flood forecasting and real-time correction in the Wujiadu-Xiaoliuxiang reach of the Huaihe Basin. The principles and construction methods of the four methods are briefly introduced, and a comparative analysis of their simulation results, especially of the stability of the flood peak, the time of occurrence of the flood peak, and the error of occurrence of the flood peak, is conducted. The conclusions are as follows: the three basic methods can improve the accuracy of flood forecasting over quite a long forecast period, and the combination method is more effective and reliable for flood peak simulation, and is more applicable to forecast correction.%选择典型的实时校正方法：传统的误差自回归、基于K最邻近算法( KNN )的非参数校正及基于Kalman滤波的多断面校正法，并以Kalman滤波与KNN结合构造综合方法，以淮河流域吴家渡-小柳巷区间作为试验河段，构建一维水动力学模型并与实时校正方法联合应用。简要介绍这4种方法的原理与模型构建方法，然后对比分析各种方法的模拟结果，尤其对模拟洪峰稳定性、峰现时间、峰现误差等进行比较，认为前3种基本方法均能在相当长的预见期内提高洪水的预报精度，而综合法实时校正法对洪峰部位的模拟更为稳定可靠、总体效果更好，更适合预报校正工作的需要。
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