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

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 mont...

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Bibliographic Details
Published inWater science & technology. Water supply Vol. 16; no. 3; pp. 845 - 854
Main Authors Luo, Jiannan, Lu, Wenxi, Ji, Yefei, Ye, Dajun
Format Journal Article
LanguageEnglish
Published London IWA Publishing 01.06.2016
Subjects
Accuracy
Artificial neural networks
Back propagation networks
Computation
Computational efficiency
Computer engineering
Data mining
Disaster management
Emergency preparedness
Geostatistics
Ground stations
Hydrologic data
Hydrology
Irrigation
Kriging interpolation
Mathematical models
Methods
Model accuracy
Neural networks
Precipitation
Prediction models
Radial basis function
Rain
Researchers
Science
Stability
Time series
Water management
Wind
Middle East
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Summary: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.
ISSN:1606-9749
1607-0798
DOI:10.2166/ws.2016.006