CAJ | 학술논문

针对水文径流预测过程中不可避免的误差，提出基于经验模式分解的卡尔曼滤波校正方法，采用经验模式分解理论将径流序列转变为平稳过程，并结合预报过程误差规律及不同物理背景，运用基于Sigma点的卡尔曼滤波校正的动态系统分析技术，对单个水文模型的输出资料进行同化操作，对状态方程进行多变量最优控制，实时修正径流预报误差。实时校正结果表明，基于经验模式分解的卡尔曼滤波校正方法能一定程度上提高径流预报精度。
침대수문경류예측과정중불가피면적오차，제출기우경험모식분해적잡이만려파교정방법，채용경험모식분해이론장경류서렬전변위평은과정，병결합예보과정오차규률급불동물리배경，운용기우Sigma점적잡이만려파교정적동태계통분석기술，대단개수문모형적수출자료진행동화조작，대상태방정진행다변량최우공제，실시수정경류예보오차。실시교정결과표명，기우경험모식분해적잡이만려파교정방법능일정정도상제고경류예보정도。
For the inevitable error existing in hydrological runoff prediction, a Kalman filter correction method based on empirical mode decomposition is presented, in which, the empirical mode decomposition is used to transform runoff into stationary process, and then combined with prediction error rule and different physical process background, the corrected dynamic system analysis technique based on Sigma point Kalman filtering is used to assimilate the output data of a single hydrological model and achieve the optimal control of multi-variable state equation to real-time correct runoff forecasting error. The real-time correction results of model show that the Kalman filter correction method based on empirical mode decomposition can improve runoff forecast accuracy.