2949 1586
Full Length Article
Journal of Intelligent Systems and Internet of Things
Volume 1 , Issue 1, PP: 5-12 , 2020 | Cite this article as | XML | Html |PDF


Hybrid Machine Learning Model for Rainfall Forecasting

Authors Names :   Hatem Abdul-Kader   1     Mustafa.Abd-El salam   2     Mona Mohamed   3  

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

    Email :  hatem6803@yahoo.com

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

    Email :  Mustafa.abdo@yahoo.com

3  Affiliation :  Information Systems Department, Higher Technological Institute, Egypt

    Email :   monmone87@hotmail.com

Doi   :   https://doi.org/10.54216/JISIoT.010101

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

References :

[1]   Pooja  Malik,  Saranjeet  Singh,  and  Binni  Arora,  “An  Effective  Weather  Forecasting Using Neural Network ,” IJEERT, Vol. 2, PP 209-212,  May 2014.

[2]  Sanjay  D.  Sawaitul,  Wagh,  and    P.  N.  Chatur,  “Classification  and  Prediction  of  Future Weather  by  using  Back  Propagation  Algorithm-An  Approach,”  IJETAE,  Vol.  2,  PP  110-113,                   January 2012. Available on: www.ijetae.com.

[3]   Rahul   Moriwal,   and   Shiv   Kumar   Dubey,   “Predicting   Weather   Using   Data   Mining Techniques,” IJAEGT, Vol. 3, pp. 51-59, February 2012.

[4]  Ch. Jyosthna  Devi,  B.  Syam  Prasad  Reddy,  K.Vagdhan  Kumar,  B.  Musala  Reddy,  and  N. Raja, Nayak, “ANN Approach for Weather Prediction using Back Propagation,”IJETT, Vol. 3, pp. 19-         23. 2012.

[5]   Folorunsho   Olaiya,   and   Adesesan   Barnabas   Adeyemo,   “Application   of   Data   Mining Techniques  in  Weather  Prediction  and  Climate  Change  Studies,”  Ijieeb,  Vol.1,  pp.  51-59,                      February 2012.

[6] Sangari.R.S, and  M.Balamurugan,” A SURVEY ON RAINFALL PREDICTION USING DATAMINING, "International Journal of Computer Science and Mobile Applications", Vol.2, PP. 84-88,               February 2014.

[7] Issam Odeh, “Temperature Prediction in Jordan using ANN,” IJCSIT, Vol. 7, PP. 378-383, 2016.

[8] Ankita Sharma, and Geeta Nijhawan," Rainfall Prediction Using Neural Network, IJCST, Vol. 3, PP. 65-69, May 2015. Available at: www.ijcstjournal.org.

[9] Mustapha BEN EL HOUARI, Omar ZEGAOUI,  and Abdelaziz ABDALLAOUI," Prediction of air temperature using Multi-layer perceptrons with Levenberg-Marquardt training algorithm,” IRJET,          Vol. 2, PP. 26-32, November 2015. Available at: www.irjet.net.

[10]  Meera  Narveka,  Priyanca  Fargose,  and  Debajyoti  Mukhopadhyay,  “Weather  Forecasting Using   ANN   with   Error   Backpropagation   Algorithm,”Springer   Link,   Proceedings   of   the                   International  Conference  on  Data  Engineering  and  Communication  Technology,  vol.  468,  PP. 629-639, August 2016.

[11]Saduf,and Mohd Arif Wani," Comparative Study of Back Propagation Learning Algorithms    for  Neural  Networks,”  ijarcsse,  Vol.  3,  PP.  1151-  1156,  December  2013.  Available  online  at: www.ijarcsse.com.

[12] Ch. Jyosthna Devi, B. Syam Prasad Reddy, K.Vagdhan Kumar, B. Musala Reddy, and N. Raja, Nayak, “ANN Approach for Weather Prediction using Back Propagation,”IJETT, Vol. 3, pp. 19-23.                2012.

 [13]  Rahul   Moriwal,   and   Shiv   Kumar   Dubey,   “Predicting   Weather   Using   Data   Mining Techniques,” IJAEGT, Vol. 3, pp. 51-59, February 2012.

[14] Magdi Zakaria, Mabrouka AL-Shebany, Shahenda Sarhan, “Artificial Neural Network : A Brief Overview,” In International Journal of Engineering Research and Applications, Vol. 4, pp.07-                         12,  February 2014.

[15] Dian Palupi Rini, Siti Mariyam Shamsuddin, and Siti Sophiyati Yuhaniz, “ Particle Swarm Optimization: Technique, System and Challenges,” IJCA, vol. 14, pp. 1-9, January 2011.

[16]  Riccardo  Poli,  James  Kennedy,  and  Tim  Blackwell,  “Particle  swarm  optimization  An overview,” Springer Science + Business Media, May 2007.

[17]  Yuhui  Shi,  and  Russell  C.  Eberhart,  “Empirical  Study  of  Particle  Swarm  Optimization,” IEEE, pp. 1945-1950, September 1999.

[18] Andrew R. Barron, ‘‘neural networks: A review Networks, from a statistical perspective,” Statistical  Science  Vol.  9,  PP.33–35,  Feb  1994.  Published  by:   Institute  of  Mathematical Statistics.

Cite this Article as :
Hatem Abdul-Kader , Mustafa.Abd-El salam , Mona Mohamed, Hybrid Machine Learning Model for Rainfall Forecasting, Journal of Intelligent Systems and Internet of Things, Vol. 1 , No. 1 , (2020) : 5-12 (Doi   :  https://doi.org/10.54216/JISIoT.010101)