Multi-component Runoff Simulation in Arid Area Based on BP Neural Network - HAN Zichen; WANG Yi; HE Bin (Renmin Zhujiang)
The surface runoff of inland rivers in arid area of Northwest China is a valuable available water resource.However,due to the complexity of runoff composition,runoff simulation is complex,and it is difficult to actually utilize the water resources.This paper applies the back-propagation (BP) neural network combined with the degree-day factor of snowmelt runoff model (SRM) to analyze and simulate the runoff process of Kaidu River Basin in Xinjiang.The results show that:①The simulation accuracy is high.The daily average temperature is the main factor affecting the runoff and process in the target river basin.②Using autocorrelation coefficient method to process daily average flow series can significantly improve the simulation accuracy of annual average daily runoff,especially for daily runoff in the snow melting period (March to May) of target river basin that cannot be effectively simulated by ordinary input.This study provides a new way for the rapid simulation of runoff supplied by glacier,snow melting and precipitation in arid area of Northwest China,and an effective reference for rational utilization of water resources.