Skip to main content

Advertisement

SpringerLink
Log in

Utility of coactive neuro-fuzzy inference system for pan evaporation modeling in comparison with multilayer perceptron

  • Original Paper
  • Published:
Meteorology and Atmospheric Physics Aims and scope Submit manuscript

Abstract

Estimation of pan evaporation (E pan) using black-box models has received a great deal of attention in developing countries where measurements of E pan are spatially and temporally limited. Multilayer perceptron (MLP) and coactive neuro-fuzzy inference system (CANFIS) models were used to predict daily E pan for a semi-arid region of Iran. Six MLP and CANFIS models comprising various combinations of daily meteorological parameters were developed. The performances of the models were tested using correlation coefficient (r), root mean square error (RMSE), mean absolute error (MAE) and percentage error of estimate (PE). It was found that the MLP6 model with the Momentum learning algorithm and the Tanh activation function, which requires all input parameters, presented the most accurate E pan predictions (r = 0.97, RMSE = 0.81 mm day−1, MAE = 0.63 mm day−1 and PE = 0.58 %). The results also showed that the most accurate E pan predictions with a CANFIS model can be achieved with the Takagi–Sugeno–Kang (TSK) fuzzy model and the Gaussian membership function. Overall performances revealed that the MLP method was better suited than CANFIS method for modeling the E pan process.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Alecsandru CD (2003) A hybrid model-based and memory-based short-term traffic prediction system. M.Sc. thesis, Civil and Environmental Engineering, Louisiana State University and Agricultural and Mechanical College, USA

  • Aytek A (2009) Co-active neurofuzzy inference system for evapotranspiration modeling. Soft Comput. doi:10.1007/s00500-008-0342-8

  • Babuska R (1998) Fuzzy modeling for control. Springer, New York

    Book  Google Scholar 

  • Box GEP, Jenkins GM (1976) Time series analysis: forecasting and control. Holden Day Inc., San Francisco

    Google Scholar 

  • Bruton JM, Mcclendon RW, Hoogenboom G (2000) Estimating daily pan evaporation with artificial neural networks. Trans ASAE 43(2):491–496

    Google Scholar 

  • Dawson CW, Abrahart RJ, Shamseldin AY, Wilby RL (2006) Flood estimation at ungauged sites using artificial neural networks. J Hydrol 319:391–409

    Article  Google Scholar 

  • Eslamian SS, Gohari SA, Biabanaki M, Malekian R (2008) Estimation of monthly pan evaporation using artificial neural networks and support vector machines. J Appl Sci 8(19):3497–3502

    Article  Google Scholar 

  • Haciismailoglu MC, Kucuk I, Derebasi N (2009) Prediction of dynamic hysteresis loops of nano-crystalline cores. Expert Syst Appl 36:2225–2227

    Article  Google Scholar 

  • Keskin ME, Terzi O (2006) Artificial neural network models of daily pan evaporation. J Hydrol Eng 11(1):65–70

    Article  Google Scholar 

  • Keskin ME, Terzi O, Taylan D (2004) Fuzzy logic model approaches to daily pan evaporation estimation in western Turkey. Hydrol Sci J 49(6):1001–1010

    Article  Google Scholar 

  • Kim S, Kim HS (2008) Neural networks and genetic algorithm approach for nonlinear evaporation and evapotranspiration modeling. J Hydrol 351:299–317

    Article  Google Scholar 

  • Kisi O (2006) Daily pan evaporation modeling using a neuro-fuzzy computing technique. J Hydrol 329:636–646

    Article  Google Scholar 

  • Kisi O (2009) Modeling monthly evaporation using two different neural computing techniques. Irrig Sci 27:417–430

    Article  Google Scholar 

  • Kumar M, Raghuwanshi NS, Singh R, Wallender WW, Pruitt WO (2002) Estimating evapotranspiration using artificial neural network. J Irrg Drain Eng ASCE 128(4):224–233

    Article  Google Scholar 

  • Leahy P, Kiely G, Corcoran G (2008) Structural optimisation and input selection of an artificial neural network for river level prediction. J Hydrol 355:192–201

    Article  Google Scholar 

  • Lenters JD, Kratz TK, Bowser CJ (2005) Effects of climate variability on lake evaporation: results from a long-term energy budget study of Sparkling Lake, northern Wisconsin (USA). J Hydrol 308:168–195

    Article  Google Scholar 

  • Marofi S, Tabari H, Zare Abyaneh H (2011) Predicting spatial distribution of snow water equivalent using multivariate non-linear regression and computational intelligence methods. Water Resour Manage 25(5):1417–1435

    Article  Google Scholar 

  • Nayak PC, Sudheer KP, Rangan DM, Ramasastri KS (2005) Short-term flood forecasting with a neurofuzzy model. Water Resour Res 41:W04004. doi:10.1029/2004WR003562

    Article  Google Scholar 

  • NeuroDimension, Inc. (2005) Developers of NeuroSolutions v5.01: Neural Network Simulator. The World Wide Web address is www.nd.com, Gainesville

  • NeuroSolutions (2003) The neural network simulation environment. NeuroDimension Inc., USA

    Google Scholar 

  • Penman HC (1948) Natural evaporation from open water, bare soil and grass. Proc R Soc Lond Ser A 193:120–145

    Article  Google Scholar 

  • Priestley CHB, Taylor RJ (1972) On the assessment of surface heat flux and evapotranspiration using large scale parameters. Mon Weather Rev 100:81–92

    Article  Google Scholar 

  • Rahimi Khoob A (2009) Estimating daily pan evaporation using artificial neural network in a semi-arid environment. Theor Appl Climatol 98(1–2):101–105

    Article  Google Scholar 

  • Rezaeian-Zadeh M, Tabari H (2012) MLP-based drought forecasting in different climatic regions. Theor Appl Climatol. doi:10.1007/s00704-012-0592-3

  • Roger JS, Chuen-Tsai S, Eiji M (1997) Neuro-fuzzy and soft-computing. Englewood Cliffs, Prentice Hall

    Google Scholar 

  • Sabziparvar AA, Tabari H (2010) Comparison of artificial neural network models and non-linear regression methods for estimation of potato crop evapotranspiration in a semi-arid region of Iran. The International Conference on Intelligent Network and Computing, November 26–28, Kuala Lumpur, Malaysia

  • Sabziparvar AA, Tabari H, Aeini A, Ghafouri M (2010) Evaluation of class A pan coefficient models for estimation of reference crop evapotranspiration in cold-semi arid and warm arid climates. Water Resour Manage 24(5):909–920

    Article  Google Scholar 

  • Smrekar J, Assadi M, Fast M, Kustrin I, De S (2009) Development of artificial neural network model for a coal-fired boiler using real plant data. Energy 34:144–152

    Article  Google Scholar 

  • Stephens JC, Stewart EH (1963) A comparison of procedures for computing evaporation and evapotranspiration. Publication 62, International Association of Scientific Hydrology. International Union of Geodynamics and Geophysics, Berkeley, pp 123–133

  • Sudheer KP, Gosain AK, Rangan D, Saheb SM (2002) Modeling evaporation using an artificial neural network algorithm. Hydrol Process 16:3189–3202

    Article  Google Scholar 

  • Sudheer KP, Gosain AK, Ramasastri KS (2003) Estimating actual evapotranspiration from limited climate data using neural computing technique. J Irrig Drain Eng ASCE 129(3):214–218

    Article  Google Scholar 

  • Tabari H, Marofi S, Sabziparvar AA (2010a) Estimation of daily pan evaporation using artificial neural network and multivariate non-linear regression. Irrig Sci 28:399–406

    Article  Google Scholar 

  • Tabari H, Marofi S, Zare Abyaneh H, Sharifi MR (2010b) Comparison of artificial neural network and combined models in estimating spatial distribution of snow depth and snow water equivalent in Samsami basin of Iran. Neur Comput Appl 19:625–635

    Article  Google Scholar 

  • Tabari H, Sabziparvar AA, Ahmadi M (2011) Comparison of artificial neural network and multivariate linear regression methods for estimation of daily soil temperature in an arid region. Meteor Atmos Phys 110:135–142

    Article  Google Scholar 

  • Terzi O, Keskin ME (2005) Modelling of daily pan evaporation. J Appl Sci 5(2):368–372

    Article  Google Scholar 

  • Torres M, Hervas C, Amador F (2005) Approximating the sheep milk production curve through the use of artificial neural networks and genetic algorithms. Comput Oper Res 32:2653–2670

    Article  Google Scholar 

  • Trajkovic S, Todorovic B, Stankovic M (2003) Forecasting of reference evapotranspiration by artificial neural network. J Irrg Drain Eng ASCE 129(6):454–457

    Article  Google Scholar 

  • Vining KC (2003) Estimation of monthly evaporation from Lake Ashtabulain North Dakota, Orwell Lake in Minnesota, and LakeTraverse in Minnesota and South Dakota, 1931–2001. Water-Resources Investigations Report, US Department of the Interior, US Geological Survey

  • Yen J, Wang L (1999) Constructing optimal fuzzy models using statistical information criteria. J Intell Fuzzy Syst 7:185–201

    Google Scholar 

Download references

Acknowledgments

The authors wish to express their gratitude to the Islamic Republic of Iran Meteorological Organization (IRIMO) for access to the weather station data. We are also grateful to the Editor and two anonymous reviewers for their helpful comments.

Author information

Authors and Affiliations

  1. Department of Water Engineering, Ayatollah Amoli Branch, Islamic Azad University, Amol, Iran

    Hossein Tabari

  2. Young Researchers Club, Hamedan Branch, Islamic Azad University, Hamedan, Iran

    P. Hosseinzadeh Talaee

  3. Department of Watershed Management, Faculty of Natural Resources, Urmia University, Urmia, Iran

    Hirad Abghari

Authors
  1. Hossein Tabari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. P. Hosseinzadeh Talaee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hirad Abghari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to P. Hosseinzadeh Talaee.

Additional information

Responsible editor: L. Gimeno.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Tabari, H., Hosseinzadeh Talaee, P. & Abghari, H. Utility of coactive neuro-fuzzy inference system for pan evaporation modeling in comparison with multilayer perceptron. Meteorol Atmos Phys 116, 147–154 (2012). https://doi.org/10.1007/s00703-012-0184-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00703-012-0184-x

Keywords

Search

Navigation