Monthly Temperature Prediction using ANNs and ANFIS (Case Study: Tehran City)

Temperature is one of the most important parameters among climate parameters. Prediction of monthly temperature is very important in water resources management, agriculture, and many other fields. For this purpose, different empirical or semi-empirical models and time series analysis are used. Recently, new intelligent methods such as ANNs and ANFIS have largely been applied in all scientific and engineering fields. In this research, the two intelligent models including the ANNs and ANFIS for monthly minimum, maximum, and mean temperatures estimation were developed in synoptic station of Tehran, Iran. Minimum, maximum, and mean data were divided to three parts including training, testing, and checking data for a month ago, two months ago, and three months ago. After introducing the data to each model, the training,

[1]  Chitralekha Mahanta,et al.  A novel approach for ANFIS modelling based on full factorial design , 2008, Appl. Soft Comput..

[2]  P. C. Nayak,et al.  A neuro-fuzzy computing technique for modeling hydrological time series , 2004 .

[3]  Ozgur Kisi,et al.  Adaptive Neurofuzzy Computing Technique for Evapotranspiration Estimation , 2007 .

[4]  J. Mubiru,et al.  Estimation of monthly average daily global solar irradiation using artificial neural networks , 2008 .

[5]  A. VasickaninovÃ,et al.  Fuzzy Model-based Neural Network Predictive Control of a Heat Exchanger , 2010 .

[6]  Suranjan Panigrahi,et al.  Artificial neural network models of wheat leaf wetness , 1997 .

[7]  Zerouak Hamza.  Applications of artificial neural-networks for energy systems , 2000 .

[8]  Bartolomeo Cosenza,et al.  Control of the biodegradation of mixed wastes in a continuous bioreactor by a type-2 fuzzy logic controller , 2009, Comput. Chem. Eng..

[9]  K. P. Sudheer,et al.  Short‐term flood forecasting with a neurofuzzy model , 2005 .

[10]  B. Karaağaç,et al.  Predicting optimum cure time of rubber compounds by means of ANFIS , 2012 .

[11]  Dan W. Patterson,et al.  Artificial Neural Networks: Theory and Applications , 1998 .

[12]  Soteris A. Kalogirou,et al.  Artificial neural networks in renewable energy systems applications: a review , 2001 .

[13]  Gerrit Hoogenboom,et al.  Neural Network Models for Predicting Flowering and Physiological Maturity of Soybean , 1994 .

[14]  Fernando Gustavo Tomasel,et al.  Prediction of functional characteristics of ecosystems: a comparison of artificial neural networks and regression models , 1997 .

[15]  R. W. McClendon,et al.  Artificial neural networks for automated year-round temperature prediction , 2009 .

[16]  Ozgur Kisi,et al.  Daily pan evaporation modelling using a neuro-fuzzy computing technique , 2006 .