基于SSA-PPR模型的河流枯季径流量变化预测方法

Forecasting Method of Runoff in Dry Season of Rivers Based on SSA-PPR Model

河流枯季径流量的实时变化影响着对其预测结果的精确性,为得到准确的预测结果,提高预测效率,提出一种基于SSA-PPR模型的河流枯季径流量变化预测方法。采用SSA-PPR模型构建河流枯季径流量变化预测的大数据统计分析模型,采用量化统计特征分析方法实现对径流量动态变化特征的挖掘,得到变化统计特征量,并结合模糊信息挖掘和自适应学习得到河流枯季径流量变化的动态解析结果。根据解析结果进行流量变化的动态分类识别,完成对河流枯季径流量变化的准确预测。仿真结果表明,本文方法的预测结果准确性较高,自适应性较好,且预测效率较高,有效提高了预测过程的收敛性,对量化分析河流枯季径流量变化具有很好的指导意义。

The real-time variability of river dry season runoff affects the accuracy of the prediction results,in order to obtain accurate prediction results and improve prediction efficiency,a method of predicting river dry season runoff change based on SSA-PPR model is proposed.This paper uses SSA-PPR model to build a big data statistical analysis model for river runoff change prediction in dry season,uses quantitative statistical feature analysis method to mine the dynamic change characteristics of runoff,and obtains the change statistical feature quantities,and combines the fuzzy information mining and adaptive learning to get the dynamic analysis results of river runoff change in dry season.According to the analysis results,the dynamic classification and recognition of the flow change are carried out,and the accurate prediction of the river runoff change in dry season is completed.The simulation results show that the prediction result of this method has higher accuracy,better adaptability and higher prediction efficiency,which effectively improves the convergence of the prediction process,and has a good guiding significance for the quantitative analysis of river runoff changes in dry season.