Because the impacts of the factors such as some disturbances are graduallyadded into the system, the grey forecast results will deviate from the systemtrue value. To improve the forecast precision, Pro-Dens Julons pro...Because the impacts of the factors such as some disturbances are graduallyadded into the system, the grey forecast results will deviate from the systemtrue value. To improve the forecast precision, Pro-Dens Julons provided twomethfor-But they had not consider the impact of artificial disturbance. LiZhihua et al. of Qinghua Univ. presented another method. This paper revisesthe method and make it be a spocial case.展开更多
Flood is one kind of unexpected and the most common natural disasters, which is affected by many factors and has complex mechanism. At home and abroad, there is still no mature theory and method used for the long-term...Flood is one kind of unexpected and the most common natural disasters, which is affected by many factors and has complex mechanism. At home and abroad, there is still no mature theory and method used for the long-term forecast of natural precipitation at present. In the present paper the disadvantages of grey GM (1, 1) and Markov chain are ana- lyzed, and Grey-Markov forecast theory about flood is put forward and then the modifying model is developed by making prediction of Chaohu Lake basin. Hydrological law was conducted based on the theoretical forecasts by grey system GM (1, 1) forecast model with improved Markov chain. The above method contained Stat-analysis, embodying scientific approach, precise forecast and its reliable results.展开更多
This paper describes the procedure of using the GM (1,1) weighted Markov chain (GMWMC) to forecast the utility water supply, a quantity that usually has significant temporal variability. The GMWMC is formulated into f...This paper describes the procedure of using the GM (1,1) weighted Markov chain (GMWMC) to forecast the utility water supply, a quantity that usually has significant temporal variability. The GMWMC is formulated into five steps: (1) use GM (1,1) to fit the trend of the data, and obtain the relative error of the fitted values; (2) divide the relative error into ‘state’ data based on pre-set intervals; (3) calibrate the weighted Markov chain model: herein the parameters are the pre-set interval and the step of transition matrix (TM); (4) by using auto-correlation coefficient as the weight, the Markov chain provides the prediction interval. Then the mid-value of the interval is selected as the relative error for the data. Upon combining the data and its relative error, the predicted magnitude in a specific time period is obtained; and, (5) validate the model. Commonly, static intervals are used in both model calibration and validation stages, usually causing large errors. Thus, a dynamic adjustment interval (DAI) is proposed for a better performance. The proposed procedure is described and demonstrated through a case study, which shows that the DAI can usually achieve a better performance than the static interval, and the best TM may exist for certain data.展开更多
文摘Because the impacts of the factors such as some disturbances are graduallyadded into the system, the grey forecast results will deviate from the systemtrue value. To improve the forecast precision, Pro-Dens Julons provided twomethfor-But they had not consider the impact of artificial disturbance. LiZhihua et al. of Qinghua Univ. presented another method. This paper revisesthe method and make it be a spocial case.
基金Under the auspices of the National Natural Science Foundation of China (No. 40571162)the Natural Science Foun-dation of Anhui Province (No. 050450401)
文摘Flood is one kind of unexpected and the most common natural disasters, which is affected by many factors and has complex mechanism. At home and abroad, there is still no mature theory and method used for the long-term forecast of natural precipitation at present. In the present paper the disadvantages of grey GM (1, 1) and Markov chain are ana- lyzed, and Grey-Markov forecast theory about flood is put forward and then the modifying model is developed by making prediction of Chaohu Lake basin. Hydrological law was conducted based on the theoretical forecasts by grey system GM (1, 1) forecast model with improved Markov chain. The above method contained Stat-analysis, embodying scientific approach, precise forecast and its reliable results.
基金Project supported by the National Natural Science Foundation of China (No. 50778121)the National Basic Research Program of China (No. 2007CB407306-1)the National Water Pollution Control and Management of Science and Technology Project of China (No. 2008ZX07317-005)
文摘This paper describes the procedure of using the GM (1,1) weighted Markov chain (GMWMC) to forecast the utility water supply, a quantity that usually has significant temporal variability. The GMWMC is formulated into five steps: (1) use GM (1,1) to fit the trend of the data, and obtain the relative error of the fitted values; (2) divide the relative error into ‘state’ data based on pre-set intervals; (3) calibrate the weighted Markov chain model: herein the parameters are the pre-set interval and the step of transition matrix (TM); (4) by using auto-correlation coefficient as the weight, the Markov chain provides the prediction interval. Then the mid-value of the interval is selected as the relative error for the data. Upon combining the data and its relative error, the predicted magnitude in a specific time period is obtained; and, (5) validate the model. Commonly, static intervals are used in both model calibration and validation stages, usually causing large errors. Thus, a dynamic adjustment interval (DAI) is proposed for a better performance. The proposed procedure is described and demonstrated through a case study, which shows that the DAI can usually achieve a better performance than the static interval, and the best TM may exist for certain data.
