Estimation of Long-Term Monthly Temperatures by Three Different Adaptive Neuro-Fuzzy Approaches Using Geographical Inputs
Publication: Journal of Irrigation and Drainage Engineering
Volume 143, Issue 12
Abstract
This paper investigates the accuracy of three different adaptive neuro-fuzzy inference systems (ANFISs), ANFIS with grid partition (ANFIS-GP), ANFIS with substructive clustering (ANFIS-SC), and ANFIS with fuzzy c means (ANFIS-FCM) in estimation of long-term monthly air temperatures. Data of 71 stations in Turkey are used in the applications. The periodicity (month of the year) and geographical variables (latitude, longitude, and altitude) are used as inputs to the models. ANFIS models are also compared with artificial neural networks (ANNs) and multilinear regression (MLR). Three ANFIS methods provide superior accuracy to ANN and MLR methods, and the ability of the ANFIS-GP is observed to be superior to the ANFIS-SC and ANFIS-FCM models. Among the ANFIS methods, the worst estimates are obtained from the ANFIS-FCM method. The maximum determination coefficients () are observed as 0.998, 0.995, and 0.995 for the ANFIS-GP, ANFIS-SC, and ANFIS-FCM at S.Urfa, Tunceli, and Usak stations, individually. The minimum values are individually found as 0.902 and 0.921 for the ANFIS-GP and ANFIS-SC at Sinop station, whereas the ANFIS-FCM model gives a minimum of 0.934 at Yalova and Yozgat stations in the testing stage. The outcomes show that long-term monthly air temperatures can be effectively assessed by the ANFIS-GP method using geographical inputs. The interpolated air temperature maps are likewise produced by using the ideal ANFIS-GP and are assessed in this study. The temperature maps demonstrate that the most noteworthy measures of temperatures happen in the southeastern, northeastern, western, and northwestern parts of Turkey.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
The third author, Professor Sungwon Kim, would like to acknowledge the grant from Dongyang University in 2015.
References
Abdel-Aal, R. E. (2004). “Hourly temperature forecasting using abductive networks.” Eng. Appl. Artif. Intell., 17(5), 543–556.
Abdulshahed, A. M., Longstaff, A. P., Fletcher, S., and Myers, A. (2015). “Thermal error modeling of machine tools based on ANFIS with fuzzy c-means clustering using a thermal imaging camera.” Appl. Math. Model., 39(7), 1837–1852.
Albayrak, S., and Fatih Amasyal, F. (2003). “Fuzzy c-means clustering on medical diagnostic systems.” Int. XII. Turkish Symp. on Artificial Intelligence and Neural Networks (TAINN), Turkey.
Ali-Nezhad, F. M., and Eskandari, H. (2012). “Effect of architectural design of greenhouse on solar radiation interception and crops growth conditions.” Int. J. Agric. Crop Sci., 4(3), 122–127.
ArcGIS version 10.2 [Computer software]. ESRI, Redlands, CA.
Baba, A. P.-A., Shiri, J., Kisi, O., Fard, A. F., Kim, S., and Amini, R. (2013). “Estimating daily reference evapotranspiration using available and estimated climatic data by adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN).” Hydrol. Res., 44(1), 131–146.
Bae, D. H., Jeong, D. M., and Kim, G. (2007). “Monthly dam inflow forecasts using weather forecasting information and neuro-fuzzy technique.” Hydrol. Sci. J., 52(1), 99–113.
Bandyopadhyay, A., Bhadra, A., Raghuwanshi, N. S., and Singh, R. (2008). “Estimation of monthly solar radiation from measured air temperature extremes.” Agric. For. Meteorol., 148(11), 1707–1718.
Bartos, I., and Janosi, I. M. (2006). “Nonlinear correlations of daily temperature records over land.” Nonlinear Process. Geophys., 13(5), 571–576.
Bilgili, M., and Sahin, B. (2009). “Prediction of long-term monthly temperature and rainfall in Turkey.” Energy Sour. Part A, 32(1), 60–71.
Cheng, C. T., Lin, J. Y., Sun, Y. G., and Chau, K. (2005). “Long-term prediction of discharges in Manwan hydropower using adaptive-network-based fuzzy inference systems models.” Advances in neural networks-ISNN, L. Wang, K. Chen, and Y. S. Ong, eds., Springer, Berlin, 1152–1161.
Cobaner, M., Citakoglu, H., Kisi, O., and Haktanir, T. (2014). “Estimation of mean monthly air temperatures in Turkey.” Comput. Electron. Agric., 109, 71–79.
Dodson, R., and Marks, D. (1997). “Daily air temperature interpolated at high spatial resolution over a large mountainous region.” Clim. Res., 8(1), 1–20.
Eftekhari, M., and Katebi, S. D. (2008). “Extracting compact fuzzy rules for nonlinear system modeling using subtractive clustering, GA and unscented filter.” Appl. Math. Modell., 32(12), 2634–2651.
El-Shafie, A., Taha, M. R., and Noureldin, A. (2007). “A neuro-fuzzy model for inflow forecasting of the Nile River at Aswan high dam.” Water Resour. Manage., 21(3), 533–556.
Genc, O., Kisi, O., and Ardiclioglu, M. (2014). “Determination of mean velocity and discharge in naturals streams using neuro-fuzzy and neural network approaches.” Water Resour. Manage., 28(9), 2387–2400.
George, R. K. (2001). “Prediction of soil temperature by using artificial neural networks algorithms.” Nonlinear Anal., 47(3), 1737–1748.
Guan, L., Yang, J., and Bell, J. M. (2007). “Cross-correlations between weather variables in Australia.” Build. Environ., 42(3), 1054–1070.
Hamid, Z. A., Moghaddamnia, A., Maryam, B. V., Adel, G., and Kisi, O. (2011). “Performance evaluation of ANN and ANFIS models for estimating garlic crop evapotranspiration.” J. Irrig. Drain. Eng., 280–286.
Hargreaves, G. H., and Samani, Z. A. (1985). “Reference crop evapotranspiration from ambient air temperature.” Proc., Winter Meeting of the American Society of Agricultural Engineers, Chicago.
Hudson, G., and Wackernagel, H. (1994). “Mapping temperature using kriging with external drift: Theory and example from Scotland.” Int. J. Climatol., 14(1), 77–91.
Ibrahim, S., Khalid, N. E. A., and Manaf, M. (2010). “Seed-based region growing (SBRG) vs adaptive network-based inference system (ANFIS) vs fuzzy c-means (FCM): Brain abnormalities segmentation.” Int. J. Electr. Comput. Eng., 5(2), 94–104.
Jang, J. S. R. (1993). “ANFIS: Adaptive-network-based fuzzy inference system.” IEEE Trans. Syst. Man Cybern., 23(3), 665–685.
Jang, J. S. R. (1996). “Input selection for ANFIS learning.” Proc., 5th IEEE Int. Conf. on Fuzzy Systems, Vol. 2, IEEE, New Orleans, LA, 1493–1499.
Jang, J. S. R., and Sun, C. T. (1995). “Neuro-fuzzy modeling and control.” Proc. IEEE, 83(3), 378–406.
Jang, J. S. R., Sun, C. T., and Mizutani, E. (1997). Neuro fuzzy and soft computing: A computational approach to learning and machine intelligence, Prentice-Hall, Upper Saddle River, NJ.
Karimi, S., Kisi, O., Shiri, J., and Makarynskyy, O. (2013). “Neuro-fuzzy and neural network techniques for forecasting sea level in Darwin Harbor, Australia.” Comput. Geosci., 52, 50–59.
Karimi-Googhari, S. H., and Lee, T. S. (2011). “Applicability of adaptive neuro-fuzzy inference systems in daily reservoir inflow forecasting.” Int. J. Soft Comput., 6(3), 75–84.
Kim, S., Shiri, J., Kisi, O., and Singh, V. P. (2013). “Estimating daily pan evaporation using different data-driven methods and lag-time patterns.” Water Resour. Manage., 27(7), 2267–2286.
Kim, S., and Singh, V. P. (2014). “Modeling daily soil temperature using data-driven models and spatial distribution concepts.” Theor. Appl. Climatol., 118(3), 465–479.
Kim, S., Singh, V. P., Lee, C. J., and Seo, Y. (2015). “Modeling the physical dynamics of daily dewpoint temperature using soft computing techniques.” KSCE J. Civil Eng., 19(6), 1930–1940.
Kiraly, A., and Janosi, I. M. (2002). “Stochastic modeling of daily temperature fluctuations.” Phys. Rev. E, 65(5), 051102.
Kisi, O., Karahan, M. E., and Şen, Z. (2006). “River suspended sediment modeling using fuzzy logic approach.” Hydrol. Process., 20(20), 4351–4362.
Kisi, O., Kim, S., and Shiri, J. (2013). “Estimation of dewpoint temperature using neuro-fuzzy and neural network techniques.” Theor. Appl. Climatol., 114(3–4), 365–373.
Kisi, O., and Sanikhani, H. (2015). “Prediction of long-term monthly precipitation using several soft computing methods without climatic data.” Int. J. Climatol., 35(14), 4139–4150.
Kisi, O., Sanikhani, H., Zounemat-Kermani, M., and Niazi, F. (2015a). “Long-term monthly evapotranspiration modeling by several data-driven methods without climatic data.” Comput. Electron. Agric., 115(1–2), 66–77.
Kisi, O., and Shiri, J. (2014). “Prediction of long-term monthly air temperature using geographical inputs.” Int. J. Climatol., 34(1), 179–186.
Kisi, O., Tombul, M., and Zounemat-Kermani, M. (2015b). “Modeling soil temperatures at different depths by using three different neural computing techniques.” Theor. Appl. Climatol., 121(1–2), 377–387.
Kisi, O., and Zounemat-Kermani, M. (2014). “Comparison of two different adaptive neuro-fuzzy inference systems in modelling daily reference evapotranspiration.” Water Resour. Manage., 28(9), 2655–2675.
Kirchner, J. W. (2006). “Getting the right answers for the right reasons: Linking measurements, analyses, and models to advance the science of hydrology.” Water Resour. Res., 42(3), W03S04.
Mamdani, E. H., and Assilian, S. (1975). “An experiment in linguistic synthesis with a fuzzy logic controller.” Int. J. Man Mach. Stud., 7(1), 1–13.
Najafzadeh, M. (2015a). “Neuro-fuzzy GMDH systems based evolutionary algorithms to predict scour pile groups in clear water conditions.” Ocean Eng., 99, 85–94.
Najafzadeh, M. (2015b). “Neurofuzzy-based GMDH-PSO to predict maximum scour depth at equilibrium at culvert outlets.” J. Pipeline Syst. Eng. Pract., 7(1), 06015001.
Ododo, J. C., Sulaiman, A. T., Aidan, J., Yuguda, M. M., and Ogbu, F. A. (1995). “The importance of maximum air temperature in the parameterisation of solar radiation in Nigeria.” Renewable Energy, 6(7), 751–763.
Piri, J., and Kisi, O. (2015). “Modeling solar radiation reached to the Earth using ANFIS, NN-ARX, and empirical models (Case studies: Zahedan and Bojnurd Stations).” J. Atmos. Solar-Terr. Phys., 123, 39–47.
Poff, N. L., Tokar, S., and Johnson, P. (1996). “Stream hydrological and ecological responses to climate change assessed with an artificial neural network.” Limnol. Ocean., 41, 857–863.
Pruitt, W. O., and Doorenbos, J. (1977). “Empirical calibration, a requisite for evapotranspiration formulae based on daily or longer mean climatic data.” Proc., Int. Round Table Conf. on Evapotranspiration, International Commission on Irrigation and Drainage, Budapest, Hungary, 20.
Rosenzweig, C., and Liverman, D. (1992). “Predicted effects of climate change on agriculture: A comparison of temperate and tropical regions.” Global climate change: Implications, challenges, and mitigation measures, Pennsylvania Academy of Science, Easton, PA, 342–361.
Sanikhani, H., Kisi, O., Nikpour, M. R., and Dinpashoh, Y. (2012). “Estimation of daily pan evaporation using two different adaptive neuro-fuzzy computing techniques.” Water Resour. Manage., 26(15), 4347–4365.
Sayed, T., Tavakolie, A., and Razavi, A. (2003). “Comparison of adaptive network based fuzzy inference systems and B-spline neuro-fuzzy mode choicemodels.” J. Comput. Civ. Eng., 123–130.
Seo, Y., Kim, S., Kisi, O., and Singh, V. P. (2015). “Daily water level forecasting using wavelet decomposition and artificial intelligence techniques.” J. Hydrol., 520, 224–243.
Seo, Y., Kim, S., and Singh, V. P. (2013). “Flood forecasting and uncertainty assessment using bootstrapped ANFIS.” Proc., 6th Conf. of Asia Pacific Association of Hydrology and Water Resources, Seoul, 1–8.
Shank, D. B., Hoogenboom, G., and McClendon, R. W. (2008). “Dewpoint temperature prediction using artificial neural networks.” J. Appl. Meteorol. Climatol., 47(6), 1757–1769.
Shiri, J., et al. (2013). “Global cross-station assessment of neuro-fuzzy models for estimating daily reference evapotranspiration.” J. Hydrol., 480, 46–57.
Shiri, J., et al. (2015). “Local vs. external training of neuro-fuzzy and neural networks models for estimating reference evapotranspiration assessed through k-fold testing.” Hydrol. Res., 46(1), 72–88.
Shiri, J., Kim, S., and Kisi, O. (2014). “Estimation of daily dewpoint temperature using genetic programming and neural networks approaches.” Hydrol. Res., 45(2), 165–181.
Shiri, J., and Kisi, O. (2010). “Short-term and long-term streamflow forecasting using a wavelet and neuro-fuzzy conjunction model.” J. Hydrol., 394(3–4), 486–493.
Smith, B. A., McClendon, R. W., and Hoogenboom, G. (2007). “An enhanced artificial neural network for air temperature prediction.” Int. J. Comput. Elec. Autom. Control Inf. Eng., 1(7), 7–12.
Tabari, H., Kisi, O., Ezani, A., and Talaee, P. H. (2012). “SVM, ANFIS, regression and climate based models for reference evapotranspiration modeling using limited climatic data in a semi-arid high land environment.” J. Hydrol., 444–445, 78–89.
Takagi, T., and Sugeno, M. (1985). “Fuzzy identification of systems and its application to modeling and control.” IEEE Trans. Syst. Man Cybern., SMC-15(1), 116–132.
Tatli, H., and Sen, Z. (1999). “A new fuzzy modeling approach for predicting the maximum daily temperature from a time series.” Turkish J. Eng. Environ. Sci., 23, 173–180.
Toros, H. (2011). “Spatio-temporal precipitation change assessments over.” Int. J. Climatol., 32(9), 1310–1325.
Unes, F., Joksimovic, D., and Kisi, O. (2015). “Plunging flow depth estimation in a stratified dam reservoir using neuro-fuzzy technique.” Water Resour. Manage., 29(9), 3055–3077.
Information & Authors
Information
Published In
Journal of Irrigation and Drainage Engineering
Volume 143 • Issue 12 • December 2017
Copyright
©2017 American Society of Civil Engineers.
History
Received: Dec 13, 2016
Accepted: May 31, 2017
Published online: Oct 11, 2017
Published in print: Dec 1, 2017
Discussion open until: Mar 11, 2018
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.