A comparison of three prediction models for predicting monthly precipitation in Liaoyuan city, China

Abstract

Accurate prediction of precipitation is of great importance for irrigation management and disaster prevention. In this study, back propagation artificial neural network (BPANN), radial basis function artificial neural network (RBFANN) and Kriging methods were applied and compared to predict the monthly precipitation of Liaoyuan city, China. An autocorrelation analysis method was used to determine model input variables first, and then BPANN, RBFANN and Kriging methods were applied to recognize the relationship between previous precipitation and later precipitation with the monthly precipitation data of 1971–2009 in Liaoyuan city. Finally, the three models' performances were compared based on models accuracy, models stability and models computational cost. Comparison results showed that for model accuracy, RBFANN performed best, followed by Kriging, and BPANN performed worst; for stability and computational cost, RBFANN and Kriging models performed better than the BPANN model. In conclusion, RBFANN is the best method for precipitation prediction in Liaoyuan city. Therefore, the developed RBFANN model was applied to predict the monthly precipitation for 2010–2019 in the study area.