Integrating metaheuristics and ANFIS for daily mean temperature forecasting

Abstract

Weather forecasting is considered as a key to successful planning for various applications such as agricultural industries. Having accurate weather forecasting allows people to make better decision on managing day to day activities. Also, it has to be underlined that forecasting is important to cope with impacts of extreme events and to adapt to climatic changes. To improve weather forecasting, we used hybrid adaptive neuro-fuzzy inference system (ANFIS), which consist in exploiting capabilities of harmony search (HS) and genetic algorithm (GA), for selecting the most relevant weather variables and simultaneously searching the most appropriate structure of ANFIS. Proposed methods are applied for six different cities of Turkey which are determined according to Aydeniz's climate classification. The results of the study showed that GA-ANFIS and HS-ANFIS yield remarkable results in daily mean temperature forecasting due to the ability of capturing the advantages of both types of methods simultaneously.