  Prompt and accurate precipitation forecast is very important for development
management of regional water resource, flood disaster prevention and people's
daily activity and production plan; however, non-linear and nonstationary
characteristics of precipitation data and noise seriously affect forecast
accuracy. This paper combines multicellular gene expression programming with
more powerful function mining ability and wavelet analysis with more powerful
denoising and extracting data fine feature capability for precipitation
forecast modeling, proposing to estimate meteorological precipitation with
WTGEPRP algorithm. Comparative result for simulation experiment with actual
precipitation data in Zhengzhou, Nanning and Melbourne in Australia indicated
that: fitting and forecasting performance of WTGEPRP algorithm is better than
the algorithm Multicellular Gene Expression Programming-based Hybrid Model for
Precipitation Prediction Coupled with EMD, Supporting Vector Regression, BP
Neural Network, Multicellular Gene Expression Programming and Gene Expression
Programming, and has good application prospect.
