838 529
Full Length Article
Volume 1 , Issue 1, PP: 5-12 , 2021


Hybrid Machine Learning Model for Rainfall Forecasting

Authors Names :   Hatem Abdel-Kader   1     Mustafa Abd-El Salam   2     Mona Mohamed   3  

1  Affiliation :  Faculty of Computers and information Menofia University Information Systems Department

    Email :  hatem6803@yahoo.com

2  Affiliation :  Faculty of Computers and Information Banha University Information Systems Department

    Email :  Mustafa.abdo@yahoo.com

3  Affiliation :  Higher Technological Institute Information Systems Department

    Email :  monmone87@hotmail.com

Doi   :  10.5281/zenodo.3376685

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.


Keywords :

Weather Forecasting , Feed Forward Neural Network; Levenberg-Marquardt; Multi-Layer Perceptron; Particle Swarm Optimization

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