基于马尔科夫链模型的小金川河流域径流形势预估分析[ 国家重点研发项目资助（2018YFB0905204）]

Prediction and analysis of runoff situation in Xiaojinchuan River Basin Based on Markov chain model

摘要:

为了分析小金川河流域中长期径流形势，本文尝试利用马尔科夫链模型进行年径流形势预估，结合历史径流的丰枯变化，计算丰水年、平水年、枯水年相互转变的马尔科夫转移概率，在此基础上，可根据当年来水情况实现对木坡电站下一年径流形势的预估分析。以2018年木坡电站的丰枯形势预估为例，经验证，该径流形势预估分析方法具有较强的实用性，为径流形势预估分析提供了一种切实可行的思路和方法。

Abstract:

In order to analyze the runoff situation of Xiaojinchuan river basin, Markov chain model is used to predict the annual runoff situation. Combined with the changes of historical inflow, by calculating the Markov transition probability of the mutual transformation of abundant, normal and dry in history, the runoff situation of Mupo hydropower station in the next year is predicted according to the inflow situation of that year. Taking the situation prediction of Mupo hydropower station in 2018 for example, the case analysis is carried out. The results show that the prediction results are consistent with the actual results, and the runoff situation prediction analysis method has strong practicability, which provides a practical idea and method for runoff situation prediction analysis.