Article: Integrating metaheuristics and artificial neural network for weather forecasting Journal: International Journal of Global Warming (IJGW) 2018 Vol.14 No.4 pp.440 - 461 Abstract: Over the years, researchers have been analysing to forecast the weather as precisely as possible in order to provide better living conditions. Nevertheless, there is no consensus on the effective weather forecasting methods and therefore, research on providing applicable and effective forecasting methods has been continued. In this study, artificial neural networks (ANNs) are integrated with two metaheuristic methods including genetic algorithm (GA) and harmony search (HS) to determine the most relevant input variables and to search the most appropriate number of hidden neurons. The proposed forecasting methods are implemented for six different cities of Turkey that are selected according to Aydeniz's climate classification. The results of the graphical analysis and performance measures show that daily mean temperature forecasting is improved by GA-ANN and HS-ANN methods due to the ability to capture the advantages of metaheuristic and ANN simultaneously. Inderscience Publishers - linking academia, business and industry through research

Title: Integrating metaheuristics and artificial neural network for weather forecasting

Authors: Mustafa Göçken; Aslı Boru; Ayşe Tuğba Dosdoğru; Mehmet Özçalıcı

Addresses: Engineering Faculty, Industrial Engineering Department, Adana Science and Technology University, 01250, Adana, Turkey ' Engineering Faculty, Industrial Engineering Department, Adana Science and Technology University, 01250, Adana, Turkey ' Engineering Faculty, Industrial Engineering Department, Adana Science and Technology University, 01250, Adana, Turkey ' Faculty of Economics and Administrative Sciences, International Trade and Logistics Department, Kilis 7 Aralık University, 79000, Kilis, Turkey

Abstract: Over the years, researchers have been analysing to forecast the weather as precisely as possible in order to provide better living conditions. Nevertheless, there is no consensus on the effective weather forecasting methods and therefore, research on providing applicable and effective forecasting methods has been continued. In this study, artificial neural networks (ANNs) are integrated with two metaheuristic methods including genetic algorithm (GA) and harmony search (HS) to determine the most relevant input variables and to search the most appropriate number of hidden neurons. The proposed forecasting methods are implemented for six different cities of Turkey that are selected according to Aydeniz's climate classification. The results of the graphical analysis and performance measures show that daily mean temperature forecasting is improved by GA-ANN and HS-ANN methods due to the ability to capture the advantages of metaheuristic and ANN simultaneously.

Keywords: artificial neural network; ANN; genetic algorithm; GA; harmony search; HS; weather forecasting.

DOI: 10.1504/IJGW.2018.091508

International Journal of Global Warming, 2018 Vol.14 No.4, pp.440 - 461

Received: 03 Feb 2016
Accepted: 24 Nov 2016
Published online: 04 May 2018 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article

Title: Integrating metaheuristics and artificial neural network for weather forecasting

Keep up-to-date