In this paper,a new ergodic property analysis model of hydrological process is proposed based on fuzzy-rough c-means clustering(FRCM),autocorrelogram,and fuzzy least absolute regression(FLAR).A precipitation time seri...In this paper,a new ergodic property analysis model of hydrological process is proposed based on fuzzy-rough c-means clustering(FRCM),autocorrelogram,and fuzzy least absolute regression(FLAR).A precipitation time series(1951―2004)from Shanghai Hydrology Station is then analyzed with the model.The results show that the precipitation time series of April,May,June,and September has er-godic property.We conclude that in the long run,the precipitation of April,May,June,and September will not keep decreasing;it will converge to its mean value in some period.展开更多
Surface precipitation estimation is very important in hydrologic forecast.To account for the influence of the neighbors on the precipitation of an arbitrary grid in the network,Bayesian networks and Markov random fiel...Surface precipitation estimation is very important in hydrologic forecast.To account for the influence of the neighbors on the precipitation of an arbitrary grid in the network,Bayesian networks and Markov random field were adopted to estimate surface precipitation.Spherical coordinates and the expectation-maximization(EM)algorithm were used for region interpolation,and for estimation of the precipitation of arbitrary point in the region.Surface precipitation estimation of seven precipitation stations in Qinghai Lake region was performed.By comparing with other surface precipitation methods such as Thiessen polygon method,distance weighted mean method and arithmetic mean method,it is shown that the proposed method can judge the relationship of precipitation among different points in the area under complicated circumstances and the simulation results are more accurate and rational.展开更多
基金Supported by the National Science and Technology Supporting Project(Grant No.2006BAB04A08)
文摘In this paper,a new ergodic property analysis model of hydrological process is proposed based on fuzzy-rough c-means clustering(FRCM),autocorrelogram,and fuzzy least absolute regression(FLAR).A precipitation time series(1951―2004)from Shanghai Hydrology Station is then analyzed with the model.The results show that the precipitation time series of April,May,June,and September has er-godic property.We conclude that in the long run,the precipitation of April,May,June,and September will not keep decreasing;it will converge to its mean value in some period.
基金supported by the National Hi-Tech Research and Development Program of China("863"Project)(Grant No.2006BAB04A08)
文摘Surface precipitation estimation is very important in hydrologic forecast.To account for the influence of the neighbors on the precipitation of an arbitrary grid in the network,Bayesian networks and Markov random field were adopted to estimate surface precipitation.Spherical coordinates and the expectation-maximization(EM)algorithm were used for region interpolation,and for estimation of the precipitation of arbitrary point in the region.Surface precipitation estimation of seven precipitation stations in Qinghai Lake region was performed.By comparing with other surface precipitation methods such as Thiessen polygon method,distance weighted mean method and arithmetic mean method,it is shown that the proposed method can judge the relationship of precipitation among different points in the area under complicated circumstances and the simulation results are more accurate and rational.
