INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 34: 179–186 (2014) Published online 4 March 2013 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/joc.3676 Prediction of long-term monthly air temperature using geographical inputs Ozgur Kisi a and Jalal Shiri b * a Civil Engineering Department, Architectural and Engineering Faculty, Canik Basari University, Samsun, Turkey b Water Engineering Department, Faculty of Agriculture, University of Tabriz, Tabriz, Iran ABSTRACT: Air temperature as a major climatic component is important in land evaluation, water resources planning and management, irrigation scheduling and agro-hydrologic planning. In this paper, the capabilities of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Networks (ANNs) were evaluated in predicting long-term monthly air temperature values at 30 weather stations of Iran. Monthly data of 20 weather stations were used for training and 10 stations’ data were used for testing. Consequently, the periodicity component, station latitude, longitude and altitude values were introduced as input variable to predict the long-term monthly temperature values. The estimates of the ANFIS and ANN models were compared with each other with respect to root mean-squared error, mean absolute error and determination coefficient statistics. The ANN models generally performed better than the ANFIS model in the test period. For the ANN model, the maximum and minimum determination coefficient values were found to be 0.995 and 0.921 in Semnan and Bandar-e-Abbas meteorological stations, respectively. The maximum and minimum determination coefficient values were found as 0.999 and 0.876 for the ANFIS model in Shiraz and Bandar-e-Abbas stations. KEY WORDS neural networks; neuro-fuzzy; geographical inputs; periodicity component Received 16 August 2012; Revised 15 December 2012; Accepted 16 January 2013 1. Introduction Prediction of air temperature is of primary importance for land evaluating and characterizing systems as well as hydrological and ecological models (Benavides et al., 2007). In such models, air temperature is applied as input parameter to derive other processes such as evapotranspi- ration, soil decomposition and plant productivity (Dodson and Marks, 1997). Accurate forecasting of this parame- ter is also needed for determining the site suitability for agricultural and forest crops, predicting of the soil sur- face temperature and avoiding the hazardous influences of temperature variations (Hudson and Wackernagel, 1994; George, 2001; Ustaoglu et al., 2008). In recent years, global warming has considerably attracted attentions of scientists. Global warming is related with an average increase in the Earth surface temperature and lower atmosphere, which in turn causes climate changes. Increasing Earth surface temperature may lead to changes in rainfall patterns, a rise in sea level, and a wide range of impacts on plants, wildlife and humans. For this reason, the importance of temperature predictions has been increased all over the World (Bilgili and Sahin, 2010). * Correspondence to: J. Shiri, Water Engineering Department, Faculty of Agriculture, University of Tabriz, Tabriz, Iran. E-mail: j_shiri2005@yahoo.com So far, a number of attempts have been carried out to model air temperature variations (Kiraly and Janosi, 2002; Bartos and Janosi, 2006; Gyure et al., 2007; Guan et al., 2009), which have emphasized the need to accurate estimation of air temperature in various aspects of meteorology, hydrology and agro-hydrology. Therefore, there are essential needs to better models with high accuracies to address the nonlinearity in air temperature variation process. In the recent past, Artificial Intelligence (AI) approaches [e.g. Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), etc] have been successfully used in a wide range of scientific applications including water resources engineering, agro-hydrology and agro-meteorology. The complete review of such applications is beyond the scope of this paper and only some relevant literature will be discussed here. Tatli and Sen (1999) introduced a fuzzy modelling approach for predicting air temperature. Abdel-Aal (2004) applied abductive neural network approach to forecast hourly air temperature. Smith et al. (2005) devel- oped an enhanced ANN for air temperature prediction by including information on seasonality and modifying parameters of an existing ANN model. Shank et al., (2008) applied neural networks for predicting dew-point temperature. Partal and Kisi (2007) introduced a new wavelet-neuro-fuzzy conjunction model for precipitation  2013 Royal Meteorological Society