Abstract: To establish a monthly average runoff forecast model for Nanzui station in West Dongting Lake with less factors, short forecast periods and high forecasting accuracy, we analyzed the relationship between the monthly average water level and runoff at Anxiang Station (Songzi-Taiping water system controlling hydrological station) and Shiguishan Station (Lishui River controlling hydrological station), and the monthly average water level at Shawan Station (Muping controlling hydrological station). Furthermore, the factor contribution rate to monthly average runoff was calculated and the input factor was determined according to the calculated correlation coefficients and factor contribution rates. Based on the above analysis, we used the PSO-BP neural network to train the average monthly runoff from 1956.1 to 2005.12 at Nanzui Station to obtain the network structure and parameters for forecasting monthly runoff from 2006.1 to 2008.12. The results showed that: ① The water level of Shiguishan and Anxiang station had the most significant effect on the monthly average runoff of Nanzui station; ② The division of non-flood and flood seasons could increase the forecast accuracy of the monthly average runoff of Nanzui Station to some extent; ③Importing the variables, including the monthly average water level and runoff at Shiguishan station and Anxiang station, and the monthly average water level at Shawan station, the PSO-BP neural network had the best forecast effect with 77.8% qualified rate and B forecast grade. ④ Importing the monthly average water level of Anxiang and Shiguishan stations and by correlation and factor contribution rate analysis, the forecasting qualified rate was reduced to 61.1%, and the forecasting level was degraded to C level, but the forecasting requirements were still met.