Abstract: 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.

Key words: SSA-PPR model, river dry season runoff, change prediction, adaptive learning, statistical analysis, dynamic analysis