The Precipitation Modeling through the CPSO-based Artificial Neural Networks

Hadi Bidokhti, Kazem Shokoohi-Mehr


Precipitation has a random chaotic nature, which is hard to model and predict due to various involved parameters. Such affecting parameters include temperature, relative humidity, pressure, radiation, the average sunny hours, the humidity of ground surface and cloudiness. Given the importance of modeling and precipitation estimate in various areas, the current study deals with an effective model using three parameters; humidity, temperature and radiation. The simulation was conducted using real data for the Multi-Layered Perceptron Networks (MLP), the Radial Basis Functions (RBF) and the Compound Neural Networks based on the Particle Swarm Optimization algorithm (CPSO-ANN). From among the advantages this method has is the separate and concurrent examination of the impact of every three entry on the network and the display of the correlations. According to the simulations, the concurrent application of all of the three entries in the network along with the CPSO-ANN leads to an effective model with the minimum of Mean Squared Error (MSE) and appropriate extension capability.


Precipitation Modeling, RBF, MLP, CPSO-ANN, MSE

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