模糊权马尔可夫链在原州区降雨量预测中的应用

Prediction of precipitation based on the Fuzzy Weighted Markov Chain in Yuanzhou Area Ningxia

依据1971—1997年宁夏固原市原州区的降雨量资料,采用均值-标准差分级法,对其进行状态分级,分为丰水年、偏丰年、平水年、偏枯年和枯水年5个状态,根据马尔可夫链预测方法,验证该降雨序列满足马氏性,并以规范化的各阶自相关系数为权,结合模糊集理论,建立适用于原州区降雨量的模糊权马尔可夫链预测模型.利用灰色系统理论进行降雨量极值年份预测,弥补了模糊权马尔可夫链预测模型对极值预测不准的缺点.结果表明,采用这种方法预测的结果与实际情况相互吻合.

Based on the annual precipitation data from 1971 to 1997 in Yuanzhou Area, taking the means and standard deviation of information series as the classification standard of precipitation, the past 27 years are classified into five states： drought year, weak drought year, normal year, weak water logging year and water logging year. After the verification of the Markov property of precipitation, with the level characteristics value of fuzzy sets, the Fuzzy weighted Markov chain model used for predicting the state of precipitation in Yuanzhou Area was established by taking standardized auto-correlation coefficient as weight coefficient, and the prediction results are satisfactory. Taking the gray system theory to predict extreme rainfall year to make up the fuzzy weighted Markov chain model for forecasting extreme disadvantage allowed. The results show that： the precipitation results are satisfactory.