|
|
|
| Application of a moving weighted Markov model in precipitation prediction |
| Mu Kai, Zhang Xiang, Yu Shilong, Li Jun, Li Yu, Dai Xiaojun |
| No.94995 Unit,Rugao 226500 |
|
|
|
|
Abstract With a data smoothing process method and mean variance analysis,the precipitation data are divided into certain groups,and the precipitation forecasting model based on Markov model is established.Taking the precipitation data of Rugao in November from 1980 to 2015 as an example,the self-correlation coefficients are calculated in order to obtain Markov weight coefficients,and then the eigenvalues are calculated by the moving weighted Markov chain.The precipitation of November in 2016 is predicted.The relative error between the predicted value and the measured value is 6.6%,which is smaller than that of the ordinary weighted Markov model,and the improvement is effective.
|
|
Received: 26 February 2019
|
|
|
|
|
|
| [1] |
Sheldon M.Ross.应用随机过程概率模型导论[M].北京:人民邮电出版社,2017.
|
| [2] |
张媛,陈震,潘尔顺,等.基于自相关观测和隐马尔科夫模型的统计过程监控[J].计算机集成制造系统,2018,24(10):2388-2394.
|
| [3] |
张熙来,赵俭辉,蔡波.针对PM2.5单时间序列数据的动态调整预测模型[J].自动化学报,2018,44(10):1790-1798.
|
| [4] |
李绍辉,孙学金.马尔科夫链在年降水量预测中的应用[J].气象水文海洋仪器,2015,32(02):84-86.
|
| [5] |
孟苓辉,刘志刚,刁利军,等.基于Markov模型的高速列车牵引传动系统可靠性评估[J].铁道学报,2016,38(08):23-27.
|
| [6] |
张祥,陈军,陆雨,等.降水量的加权型马尔科夫模型预测[J].气象水文海洋仪器,2018,35(02):18-20.
|
|
|
|
2019, Vol. 36 
