Application of Genetic Algorithm to Optimize the Performance of Adaptive Neural - Fuzzy Inference System in order to predict maximum of air temperature (Case study: Isfahan city)

Document Type : Research Article


1 M.Sc. Student, Faculty of Civil Engineering, Semnan University, Semnan, Iran.

2 M.Sc. Student, Faculty of Civil Engineering, Semnan University, Semnan, Iran

3 Assistant Professor, Faculty of Civil Engineering, Semnan University, Semnan


In this study, the use of genetic optimization algorithm (GA),Particle Swarm(PSO), the ant colony for continuum (ACOR)and differential evolution (DE),to develop and improve the performance of ANFIS were investigated. the monthly maximum temperatures in Isfahan during the period of 64 years (1951-2014), was simulated and analyzed. At first in a sensitivity analysis, the best entries for each prediction period (1 month, 1, 2 and 3 years) were selected. Then, the maximum temperature hybrid models by ANFIS-GA,ANFIS-PSO,ANFIS-DE,ANFIS-ACOR and ANFIS were examined. The performance of each model with regard to R2, RMSE and MAE were evaluated. The results showed that the ANFIS-GA, as the most appropriate model, increased ANFIS performance in R2 to by 0.06, 0.07, 0.08 and 0.12 and RMSE by 0.09, 0.09, 0.16 and 0.1, respectively, in 1 month and 1, 2 and 3 year. After, ANFIS-DE and ANFIS-PSO, respectively, had the best forecasting accuracy. On the other hand, ANFIS showed highest error and lowest R2, as the weakest model. The results showed that the proposed models, which use global search techniques and avoid being trapped in local optimum, could improve the performance of ANFIS favorably.Therefore, these models can be used in other areas related to hydrology and water resources.


Main Subjects

[1]. Asakareh H. ARIMA modeling of annual mean temperature of Tabriz city. Geographical Research. 2009; 47: 123-131.
[2]. Benavides R, Montes F, Rubio A , Osoro K. Geostatistical modeling of air temperature in a mountainous region of northern Spain. Agricultural and Forest Meteorology. 2007; 146(3-4): 173-188.
[3]. Jain AK. Mao J, Mohiuddin KM.. Artificial neural networks: A tutorial. Computer, IEEE. 1996: 31-44.
[4]. Peyghami MR, Khanduzi R. Novel MLP neural network with hybrid tabu search algorithm. Neural Network World. 2013; 3(13): 255-270.
[5]. Pousinho HMI, Mendes VMF, Catalão JPS. Hybrid PSO-ANFIS Approach for Short-Term Electricity Prices Prediction. In Proceedings of the 2010 PES general meeting, Michigan. 2010: 1-6.
[6]. Sheikhan M, Mohammadi N. Time series prediction using PSO-optimized neural network and hybrid feature selection algorithm for IEEE load data. Neural computing and applications. 2013;23(3-4): 1185-1194.
[7]. Cheng CHT, Niu WJ, Feng ZK, Shen J, Chau KW. Daily Reservoir Runoff Forecasting Method Using Artificial Neural Network Based on Quantum-behaved Particle Swarm Optimization. Water. 2015; 7: 4232- 4246.
[8]. Jalalkamali A. Using of hybrid fuzzy models to predict spatiotemporal groundwater quality parameters. Earth Science Informatics. 2015; 8(4): 885-894.
[9]. Rezapour Tabari M M. Prediction of River Runoff Using Fuzzy Theory and Direct Search Optimization Algorithm Coupled Model. Arabian Journal for Science and Engineering. 2016; 41(10): 4039-4051.
[10]. Behmanesh M, Mohammadi M. Adaptive Neuro-Fuzzy Inference System with Self-Feedback and Imperialist Competitive Learning Algorithm for Chaotic Time Series Prediction. Journal of Computational Intelligence in Electrical Engineering. 2016; 4(7): 13-30.
[11]. Azad A, Karami H, Farzin S, Saeedian A, Kashi H, Sayyahi H. Prediction of water quality parameters using ANFIS optimized by intelligence algorithms (Case study: Gorganrood River). KSCE Civil engineering Journal. 2017; 1-8. DOI 10.1007/s12205-017-1703-6. [Persian]
[12]. Salahi B, Hoseini SA, Shayeghi H, Sobhani B. Prediction of maximum temperatures using artificial neural network model. Geographic research. 2010; 25(3): 57-78. [Persian]
[13]. Tektas M. Weather Forecasting Using ANFIS and ARIMA Models, A Case Study for Istanbul. Environmental Research, Engineering and Management. 2010; 51:5-10.
[14]. Ghorbani MA, Kazemi H, Farsadizadeh D, Yousefi P. Prediction of Air Temperature Using Artificial Intelligent Methods. Journal of Engineering and Applied Sciences. 2012; 7(2): 134-142.
[15]. Kisi O, Kim S, Shiri J. Estimation of dew point temperature using neuro-fuzzy and neural network techniques. Theoretical and Applied Climatology. 2013; 114(3-4): 365-373.
[16]. Daneshmand H, Tavousi T, Khosravi M, Tavakkoli S. Modeling minimum temperature via adaptive 4 neuro-fuzzy inference system method based 5 on spectral analysis of climate indices. Journal of the Saudi Society of Agricultural Sciences. 2015; 14(1): 33-40.
 [17]. Mohammadi K, Shamshirband Sh, Tong CW, Arif M, Petkovic Ch. A new hybrid support vector machine–wavelet transform approach for estimation of horizontal global solar radiation. Energy Conversion and Management. 2015; 92: 162-171.
[18]. Kisi O, Sanikhani H. Modelling long-term monthly temperatures by several data-driven methods using geographical inputs. International Journal of Climatology. 2015; DOI: 10.1002/joc.4249.
[19]. Shafaghi S. Geography of Isfahan. 2nd ed. University of Esfahan. Esfahan. 2003. [Persian]
[20]. Zadeh LA. Fuzzy sets. InformationandControl. 1965; 8(3): 338-353.
[21]. Jang JSR. ANFIS: Adaptive-network-based fuzzy inference system. Systems, Man and Cybernetics, IEEE Transactions. 1993; 23(3), 665-685.
[22]. Storn R, Price K. Differential Evolution-A Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces. Technical report, International Computer Science, Berkeley. 1995.
[23]. Dashti R, Sattari M T, Nourani V. Performance evaluation of differential evolution algorithm in optimum operating of Eleviyan single-reservoir dam system. Journal of Protection of water and soil resources. 2017; 6(3): 61-76.
[24]. Holland JH. Adaption in natural and artificial system. The University of Michigan Press. 1975.
[25]. Jaramillo J, Bhadury J, Batta R. On the use of genetic algorithms to solve location problems. Computers & Operations Research. 2002; 29: 761-779.
[26]. Eberhart R, Kennedy J. A New Optimizer Using Particle Swarm Theory. Sixth International Symposium on Micro Machine and Human Science, IEEE. 1995.
[27]. Golmakani H, Fazel M. Constrained Portfolio Selection using Particle Swarm Optimization. Expert Systems with Applications. 2011; 38: 8327–8335.
[28]. Dorigo M. Optimization, Learning and Natural Algorithms. Ph.D Thesis. Dipartimento di Elettronica, Politecnico di Milano, Italy. 1992.
[29]. Socha K, Dorigo M. Ant colony optimization for continuous domains. European Journal of Operational Research. 2008; 185: 1155-1173.
[30]. Deb K A P, Agarwal S, Meyarivan T. A Fast Elitist Multi-Objective Genetic Algorithm:NSGA-II. IEEE Transactions on Evolutionary Computation.2000; 6: 182-197.
Volume 5, Issue 3
October 2018
Pages 763-775
  • Receive Date: 17 September 2017
  • Revise Date: 03 November 2017
  • Accept Date: 02 January 2018
  • First Publish Date: 23 September 2018