SPBO-ANFIS Model of Combined Monthly Runoff Forecasting Based on Singular Spectrum Analysis

In view of the multi-scale non-stationarity and other characteristics of monthly runoff in hydrological time series,this paper proposes a singular spectrum decomposition (SSD)-based model of combined monthly runoff forecasting that integrates the student psychology based optimization (SPBO) algorithm with the adaptive network based fuzzy inference system (ANFIS),namely the SSD-SPBO-ANFIS model.This model is then applied to the monthly runoff forecasting at a hydrological station in Yunnan Province.Specifically,time series data of sample monthly runoff are decomposed into various independent sub-series components through SSD to reduce the complexity of the time series data;then,the principle of the SPBO algorithm is outlined,and eight standard functions are selected for simulation verification and comparison of the SPBO algorithm;finally,the SPBO algorithm is employed to optimize the ANFIS condition and conclusion parameters.The SSD-SPBO-ANFIS model is built to forecast each sub-series,which is then superimposed to obtain the final monthly runoff forecasting result.In addition,the results of the proposed model are compared with those of the ensemble empirical mode decomposition (EEMD)-based EEMD-SPBO-ANFIS model and the SPBO-ANFIS model without decomposition.The following observations can be made from the results:The SPBO algorithm has favorable optimization accuracy;with a mean absolute percentage error of 5.57%,a mean absolute error of 0.20 m³/s,a Nash coefficient of 0.994 8,and a pass rate of 96.7%,the SSD-SPBO-ANFIS model has an effect better than that of the EEMD-SPBO-ANFIS model and far better than that of the SPBO-ANFIS model in forecasting sample monthly runoff.The proposed model and method can provide references for related research on hydrological time series forecasting.