Application of Soft Computing Models to Daily Average Temperature Analysis

Yıl 2015, Cilt: 1 Sayı: 2, 56 - 64, 02.04.2015

Mustafa Göçken , Aslı Boru , Ayşe Tuğba Dosdoğru Nafiz Berber

https://doi.org/10.19072/ijet.105706

Öz

Providing critical information about daily life, weather forecasting has important role for human being. Especially, temperature forecasting is rather important because it affects not only people but also other atmospheric parameters. Various techniques have been used for analysis of the dynamic behaviour of weather. This ranges from simple observation of weather to using computer technology. In this study, ANFIS (Adaptive Network Based Fuzzy Inference System), ANN (Artificial Neural Network) and MRA (Multiple Regression Analysis) have been applied for weather forecasting. To judge the forecasting capability of the proposed models, the graphical analysis and the indicators of the accuracy of Mean Absolute Deviation (MAD), Mean Square Error (MSE), Root-Mean Squared Error (RMSE), Mean Absolute Percent Error (MAPE), Determination Coefficient (R²), Index of Agreement (IA), Fractional Variance (FV), Coefficient of Variation (CV, %) are given to  describe  models’ forecasting  performance  and  the  error. The results show that ANFIS exhibited best forecasting performance on weather forecasting compared to ANN and MRA.

Anahtar Kelimeler

Weather forecasting, ANFIS, ANN, MRA

Kaynakça

• G. Shrivastava, S. Karmakar, M. K. Kowar, and P. Guhathakurta, Application of Artificial Neural Networks in weather forecasting: A comprehensive literature review, International Journal of Computer Applications, vol. 51, no. 18, pp. 0975-8887, 2012.
• Paras, and S. Mathur, A simple weather forecasting model using mathematical regression, Indian Research Journal of Extension Education Special Issue 1, pp. 161–168, 2012.
• J. T. Abatzoglou, D. E. Rupp, and P. W. Mote, Seasonal climate variability and change in the Pacific Northwest of the United States, American Meteorological Society, vol. 27, pp. 2125–2142, 2014.
• I. Maqsood, M. R. Khan, and A. Abraham, Weather forecasting models using ensembles of neural networks, Intelligent Systems Design and Applications Advances in Soft Computing, vol. 23, pp. 33–42, 2003.
• K. Abhishek, M. P. Singh, S. Ghosh, and A. Anand, Weather forecasting model using Artificial Neural Network, Procedia Technology, vol. 4, pp. 311–318, 2012.
• Ö. A. Dombaycı, and M. Gölcü, Daily means ambient temperature prediction using Artificial Neural Network method: A case study of Turkey, Renewable Energy, vol. 34, pp. 1158–1161, 2009.
• B. A. Smith, G. Hoogenboom, and R. W. McClendon, Artificial Neural Networks for automated year-round temperature prediction, Computers and Electronics in Agriculture, vol. 68, pp. 52–61, 2009.
• B. A. Smith, R. W. McClendon, and G. Hoogenboom, An enhanced Artificial Neural Network for air temperature prediction, World Academy of Science, Engineering and Technology, vol.1 no.7, pp. 80–85, 2005.
• M. Şahin, Modeling of air temperature using remote sensing and Artificial Neural Network in Turkey, Advances in Space Research, vol. 50, no. 7, pp. 973–985, 2012.
• M. Hayati, and Z. Mohebi, Application of Artificial Neural Networks for temperature forecasting, World Academy of Science, Engineering and Technology, vol.1, no.4, pp. 654–658, 2007.
• A. Kaur, and H. Singh, Artificial Neural Networks in forecasting minimum temperature, International Journal of Electronics & Communication Technology, vol. 2, no. 3, pp. 101–105, 2011.
• D. Domanska, and M. Wojtylak, Fuzzy weather forecast in forecasting pollution concentrations, Proc. of Chaotic Modeling and Simulation International Conference, pp. 1–8, 2010.
• M. Tektaş, Weather forecasting using ANFIS and ARIMA models, Environmental Research, Engineering and Management, vol. 51, pp. 5–10, 2010.
• H. Daneshmand, T. Tavousi, M. Khosravi, and S. Tavakoli, Modeling minimum temperature using Adaptive Neuro-Fuzzy Inference System based on spectral analysis of climate indices: A case study in Iran, Journal of the Saudi Society of Agricultural Sciences, vol. 14, pp. 33–40, 2015.
• O. F. Oyediran, and A. B. Adeyemo, Performance evaluation of Neural Network MLP and ANFIS models for weather forecasting studies, African Journal of Computing & ICT, vol. 6, no. 1, pp. 147–164, 2013.
• R. May, G. Dandy, and H. Maier, Review of input variable selection methods for Artificial Neural Networks, Artificial Neural Networks-Methodological Advances and Biomedical Applications, pp. 19–44, 2011.
• N. Sharma, P. Sharma, D. Irwin, and P. Shenoy, Predicting Solar Generation from Weather Forecasts Using Machine Learning, Distributed Generation, Microgrids, Renewables and Storage (IEEE SmartGridComm), pp. 528- 533, 2011.
• R. Taylor, Interpretation of the correlation coefficient: A basic review, Journal of Diagnostic Medical Sonography, vol. 6, pp. 35–39, 1990.
• S. Prion, and K. A. Haerling, Making sense of methods and measurement: pearson product-moment correlation coefficient. Clinical Simulation in Nursing, vol. 10, pp. 587–588, 2014.
• Dr G. S. V. P. Raju, V. M. Sumalatha, K. V. Ramani, and K. V. Lakshmi, Solving uncertain problems using ANFIS, International Journal of Computer Applications, vol. 29, no. 11, 2011.
• R. Sivakumar, C. Sahana, and P. A. Savitha, Design of ANFIS based estimation and control for MIMO systems, International Journal of Engineering Research and Applications, vol. 2, no. 3, pp. 2803–2809, 2012.
• C. Jeong, J. Shin, T. Kim, and J. Heo, Monthly precipitation forecasting with a Neuro-Fuzzy model, Water Resources Management, vol. 26, pp. 4467–4483, 2012.
• M. Chen, and B. Chen, Online fuzzy time series analysis based on entropy discretization and a Fast Fourier Transform, Applied Soft Computing, vol.14, pp. 156–166, 2014.
• M. Y. Chen, D. R. Chen, M. H. Fan, and T. Y. Huang, International transmission of stock market movements: an Adaptive Neuro-Fuzzy Inference System for analysis of TAIEX forecasting, Neural Computing and Applications, vol. 23, pp. 369–378, 2013.
• S. Roy, Design of Adaptive Neuro-Fuzzy Inference System for predicting surface roughness in turning operation, Journal of Scientific and Industrial Research, vol. 64, pp. 653–659, 2005.
• S. Mandal, J. Choudhury, and S. Chaudhuri, In search of suitable fuzzy membership function in prediction of time series data, International Journal of Computer Science Issues, vol. 9, no 3, pp. 293–302, 2012.
• B. Khoshnevisan, S. Rafiee, M. Omid, and H. Mousazadeh, Development of an intelligent system based on ANFIS for predicting wheat grain yield on the basis of energy inputs, Information Processing in Agriculture, vol. 1, pp. 14-22, 2014.
• D. Graupe, Principles of Artificial Neural Networks, Advanced Series on Circuits and Systems, 2st ed., vol. 6, New Jersey: World Scientific, 2007.
• J. M. Zurada, Introduction to Artificial Neural Systems, St. Paul: West Publishing Company, vol. 8, 1992.
• A. Krenker, J. Bester, and A. Kos, Introduction to the Artificial Neural Networks. Artificial Neural Networks: Methodological Advances and Biomedical Applications, First ed., Intech, India, pp 1–18, 2011.
• T. M. Choi, Fashion Branding and Consumer Behaviors: Scientific Models, Imprint: Springer, 2014.
• D. Şchiopu, E. G. Petre, and C. NegoiŃă, Weather forecast using SPSS statistical methods, Petroleum-Gas University of Ploiesti Bulletin, Mathematics-Informatics-Physics, vol. 61, no. 1, pp. 97–100, 2009.
• Z. Z. Latt, and H. Wittenberg, Improving flood forecasting in a developing country: a comparative study of Stepwise Multiple Linear Regression and Artificial Neural Network, Water Resource Management, vol. 28, pp. 2109–2128, 2014.
• H. R. Stanski, W. R. Burrows, and L. J. Wilson, Survey of common verification methods in meteorology, (Second Edition), World Meteorological Organization, 1989.
• T. Chai, and R. Draxler, Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature, Geoscientific Model Development, vol. 7, pp. 1247–1250, 2014.
• S. Makrıdakıs, and M. Hıbon, Evaluating accuracy (or error) measures, INSEAD, 1995.
• E. Woschnagg, Evaluating forecast accuracy, University of Vienna, Department of Economics, 2004.
• A. Bhatt, D. Pant, and R. Singh, An analysis of the performance of Artificial Neural Network technique for apple classification, AI & Soc. vol. 29, 103–111, 2014.
• P. Krause, D. P. Boyle, and F. Bäse, Comparison of different efficiency criteria for hydrological model assessment, Advances in Geosciences, vol.5, pp. 89–97, 2005.
• C. J. Willmott, On the validation of models. Physical Geography, vol. 2, pp. 194–194, 1981.
• H. Atmaca, B. Cetisli, and H. S. Yavuz, The comparison of fuzzy inference systems and neural network approaches with ANFIS method for fuel consumption data, Second International Conference on Electrical and Electronics Engineering Papers ELECO, 2001.