一种北江流域年降雨量的权马尔可夫链预测模型  

Annual Precipitation Forecasting Based on the Weighted Markov Chain in Beijiang River Basin

作　　者：刘德地[1] 陈晓宏[1] 

机构地区：[1]中山大学水资源与环境研究中心,广东广州510275

出　　处：《水文》2006年第6期23-26,96,共5页Journal of China Hydrology

基　　金：国家自然科学基金资助项目(50579078)

摘　　要：根据北江流域的48个站点的年降雨量资料和泰森多边形计算方法,计算出北江流域的面降雨量。再结合丰、偏丰、平、偏枯、枯水年的频率标准,建立了适用于北江流域年降雨量的分级数值区间,同时,验证了该序列满足马尔可夫链的要求,并考虑该年降雨量序列是相依随机变量的特点,以规范化的各阶自相关系数为权,建立了北江流域年降雨量的权马尔可夫链预测模型,实例验证结果令人满意。Based on the annual precipitation data from 48 rain gauging stations in Beijiang River Basin and the method of Thiessen, no-point annual precipitation have been calculated. Combined the classification standard of precipitation, the numeral range of classification which is fitted to Beijiang River Basin has been established. Then based on the verification of the Markov chain characteristics of precipitation, the weighted Markov chain used for predicting the state of precipitation in Beijiang River Basin has been developed. And the results of prediction are satisfied.

关 键 词：年降雨量 权马尔可夫链 预测 北江流域 

分 类 号：P338.9[天文地球—水文科学]