Hybrid Machine Learning Model for Rainfall Forecasting
Hatem Abdul-Kader, Mustafa Abd-Elsalam, Mona A. Mohamed
Abstract
The state of the weather became a point of attraction for researchers in recent days. It control in many fields as agriculture, the country determines the types of crops depend on state of the atmosphere. It is therefore important to know the weather in the coming days to take precautions. Forecasting the weather in future especially rainfall won the attention of many researchers, to prevent flooding and other risks arising from rainfall. This Paper presents a vigorous hybrid technique was applied to forecast rainfall by combining Particle Swarm Optimization (PSO) and Multi-Layer Perceptron (MLP) which is popular kind used in Feed Forward Neural Network (FFNN). The purpose of using PSO with MLP is not just to forecast the rainfall but, to improve the performance of the network; this was proved by comparison with various Back Propagation (BP) an algorithm such as Levenberg-Marquardt (LM) through results of Root Mean Square Error (RMSE). RMSE for MLP based PSO is 0.14 while RMSE for MLP based LM is 0.18.