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Compared with traditional real-time forecasting,this paper proposes a Grey Markov Model(GMM) to forecast the maximum water levels at hydrological stations in the estuary area.The GMM combines the Grey System and Markov theory into a higher precision model.The GMM takes advantage of the Grey System to predict the trend values and uses the Markov theory to forecast fluctuation values,and thus gives forecast results involving two aspects of information.The procedure for forecasting annul maximum water levels with the GMM contains five main steps:1) establish the GM(1,1) model based on the data series;2) estimate the trend values;3) establish a Markov Model based on relative error series;4) modify the relative errors caused in step 2,and then obtain the relative errors of the second order estimation;5) compare the results with measured data and estimate the accuracy.The historical water level records(from 1960 to 1992) at Yuqiao Hydrological Station in the estuary area of the Haihe River near Tianjin,China are utilized to calibrate and verify the proposed model according to the above steps.Every 25 years’ data are regarded as a hydro-sequence.Eight groups of simulated results show reasonable agreement between the predicted values and the measured data.The GMM is also applied to the 10 other hydrological stations in the same estuary.The forecast results for all of the hydrological stations are good or acceptable.The feasibility and effectiveness of this new forecasting model have been proved in this paper.
Compared with traditional real-time forecasting, this paper proposes a Gray Markov Model (GMM) to forecast the maximum water levels at hydrological stations in the estuary area. The GMM combines the Gray System and Markov theory into a higher precision model. GMM takes advantage of the Gray System to predict the trend values and uses the Markov theory to predict fluctuation values, and thus gives the correlation results involving two aspects of information. The procedure for forecasting annul maximum water levels with the GMM contains five main steps: 1) establish the GM (1,1) model based on the data series; 2) estimate the trend values; 3) establish a Markov Model based on relative error series; 4) modify the relative errors caused in step 2, and then the relative errors of the second order estimation; 5) compare the results with measured data and estimate the accuracy. historical the level water records (from 1960 to 1992) at Yuqiao Hydrological Station in the estuary area of the Haihe River n ear Tianjin, China are utilized to calibrate and verify the proposed model according to the above steps. Every 25 years’ data are considered as a hydro-sequence. Light groups of simulated results show reasonable agreement between the predicted values and the measured data. GMM is also applied to the 10 other hydrological stations in the same estuary. The forecast results for all of the hydrological stations are good or acceptable. The feasibility and effectiveness of this new forecasting model have been proven in this paper.