Integrating metaheuristics and artificial neural network for weather forecasting. (2018)

• Record Type: Journal Article
• Authors: Göçken, Mustafa; Boru, Aslı; Dosdoğru, Ayşe Tuğba; Özçalıcı, Mehmet
• Is Part Of: International journal of global warming. Volume 14:Number 4(2018)
• Page Start: 440
• Page End: 461
• Publication Date: 2018
• Subjects: artificial neural network -- ANN -- genetic algorithm -- GA -- harmony search -- HS -- weather forecasting; Global warming -- Periodicals; Global warming -- Statistics -- Periodicals; Global warming -- Economic aspects -- Periodicals; Climatic changes -- Periodicals; Climatic changes -- Economic aspects -- Periodicals; Climatic changes -- Environmental aspects -- Periodicals; 363.73874
• Languages: English
• ISSNs: 1758-2083

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.