Table of Contents Author Guidelines Submit a Manuscript
Advances in Meteorology
Volume 2017, Article ID 5356324, 9 pages
https://doi.org/10.1155/2017/5356324
Research Article

A New Method for Evaporation Modeling: Dynamic Evolving Neural-Fuzzy Inference System

1School of Natural Sciences and Engineering, Ilia State University, Tbilisi, Georgia
2Department of Civil Engineering, Birjand University of Technology, Birjand, Iran
3Department of Civil and Environmental Engineering, Incheon National University, Incheon 22012, Republic of Korea
4Incheon Disaster Prevention Research Center, Incheon National University, Incheon 22012, Republic of Korea

Correspondence should be addressed to Jong Wan Hu; rk.ca.noehcni@42pgnoj

Received 8 May 2017; Revised 24 July 2017; Accepted 20 August 2017; Published 27 September 2017

Academic Editor: Francesco Viola

Copyright © 2017 Ozgur Kisi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. L. Monteith, “Evaporation and environment,” Symposia of the Society for Experimental Biology, vol. 19, pp. 205–234, 1965. View at Google Scholar · View at Scopus
  2. H. L. Penman, “Natural evaporation from open water, hare soil and grass,” Proceedings of the Royal Society of London. Series A, Mathematical and physical sciences, vol. 193, no. 1032, pp. 120–145, 1948. View at Publisher · View at Google Scholar · View at Scopus
  3. H. Sanikhani, O. Kisi, M. R. Nikpour, and Y. Dinpashoh, “Estimation of daily pan evaporation using two different adaptive neuro-fuzzy computing techniques,” Water Resources Management, vol. 26, no. 15, pp. 4347–4365, 2012. View at Publisher · View at Google Scholar · View at Scopus
  4. M. L. Roderick and G. D. Farquhar, “Changes in Australian pan evaporation from 1970 to 2002,” International Journal of Climatology, vol. 24, no. 9, pp. 1077–1090, 2004. View at Publisher · View at Google Scholar · View at Scopus
  5. M. L. Roderick, L. D. Rotstayn, G. D. Farquhar, and M. T. Hobbins, “On the attribution of changing pan evaporation,” Geophysical Research Letters, vol. 34, no. 17, Article ID L17403, 2007. View at Publisher · View at Google Scholar · View at Scopus
  6. O. Kisi, “Daily pan evaporation modelling using a neuro-fuzzy computing technique,” Journal of Hydrology, vol. 329, no. 3-4, pp. 636–646, 2006. View at Publisher · View at Google Scholar · View at Scopus
  7. A. El-Shafie, H. M. Alsulami, H. Jahanbani, and A. Najah, “Multi-lead ahead prediction model of reference evapotranspiration utilizing ANN with ensemble procedure,” Stochastic Environmental Research and Risk Assessment, vol. 27, no. 6, pp. 1423–1440, 2013. View at Publisher · View at Google Scholar · View at Scopus
  8. M. K. Goyal, B. Bharti, J. Quilty, J. Adamowski, and A. Pandey, “Modeling of daily pan evaporation in sub tropical climates using ANN, LS-SVR, fuzzy logic, and ANFIS,” Expert Systems with Applications, vol. 41, no. 11, pp. 5267–5276, 2014. View at Publisher · View at Google Scholar · View at Scopus
  9. X. Li, H. R. Maier, and A. C. Zecchin, “Improved PMI-based input variable selection approach for artificial neural network and other data driven environmental and water resource models,” Environmental Modelling and Software, vol. 65, pp. 15–29, 2015. View at Publisher · View at Google Scholar · View at Scopus
  10. X. Li, A. C. Zecchin, and H. R. Maier, “Selection of smoothing parameter estimators for general regression neural networks - Applications to hydrological and water resources modelling,” Environmental Modelling and Software, vol. 59, pp. 162–186, 2014. View at Publisher · View at Google Scholar · View at Scopus
  11. A. Moghaddamnia, M. Ghafari Gousheh, J. Piri, S. Amin, and D. Han, “Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques,” Advances in Water Resources, vol. 32, no. 1, pp. 88–97, 2009. View at Publisher · View at Google Scholar · View at Scopus
  12. J. Shiri, W. Dierickx, A. Pour-Ali Baba, S. Neamati, and M. A. Ghorbani, “Estimating daily pan evaporation from climatic data of the State of Illinois, USA using adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN),” Hydrology Research, vol. 42, no. 6, pp. 491–502, 2011. View at Publisher · View at Google Scholar · View at Scopus
  13. Ö. Terzi, M. E. Keskin, and E. D. Taylan, “Estimating evaporation using ANFIS,” Journal of Irrigation and Drainage Engineering, vol. 132, no. 5, pp. 503–507, 2006. View at Publisher · View at Google Scholar · View at Scopus
  14. I. Mansouri, A. Gholampour, O. Kisi, and T. Ozbakkaloglu, “Evaluation of peak and residual conditions of actively confined concrete using neuro-fuzzy and neural computing techniques,” Neural Computing and Applications, pp. 1–16, 2016. View at Publisher · View at Google Scholar · View at Scopus
  15. I. Mansouri and O. Kisi, “Prediction of debonding strength for masonry elements retrofitted with FRP composites using neuro fuzzy and neural network approaches,” Composites Part B: Engineering, vol. 70, pp. 247–255, 2015. View at Publisher · View at Google Scholar · View at Scopus
  16. I. Mansouri, T. Ozbakkaloglu, O. Kisi, and T. Xie, “Predicting behavior of FRP-confined concrete using neuro fuzzy, neural network, multivariate adaptive regression splines and M5 model tree techniques,” Materials and Structures, vol. 49, no. 10, pp. 4319–4334, 2016. View at Publisher · View at Google Scholar · View at Scopus
  17. P. B. Shirsath and A. K. Singh, “A comparative study of daily pan evaporation estimation using ANN, regression and climate based models,” Water Resources Management, vol. 24, no. 8, pp. 1571–1581, 2010. View at Publisher · View at Google Scholar · View at Scopus
  18. F.-J. Chang, L.-C. Chang, H.-S. Kao, and G.-R. Wu, “Assessing the effort of meteorological variables for evaporation estimation by self-organizing map neural network,” Journal of Hydrology, vol. 384, no. 1-2, pp. 118–129, 2010. View at Publisher · View at Google Scholar · View at Scopus
  19. T. Furuhashi, T. Hasegawa, S. Horikawa, and Y. Uchikawa, “An adaptive fuzzy controller using fuzzy neural networks,” in Proceedings of the Fifth IFSA World Congress, Seoul, South Korea, 1993.
  20. C. Lin and C. Lee, Neuro Fuzzy Systems, Prentice Hall, Englewood Cliffs, NJ, USA, 1996.
  21. E. Lughofer, Evolving Fuzzy Systems - Methodologies, Advanced Concepts and Applications, Springer, India, 2011.
  22. P. T. Nastos, N. Politi, and J. Kapsomenakis, “Spatial and temporal variability of the Aridity Index in Greece,” Atmospheric Research, vol. 119, pp. 140–152, 2013. View at Publisher · View at Google Scholar · View at Scopus
  23. N. K. Kasabov and Q. Song, “DENFIS: Dynamic evolving neural-fuzzy inference system and its application for time-series prediction,” IEEE Transactions on Fuzzy Systems, vol. 10, no. 2, pp. 144–154, 2002. View at Publisher · View at Google Scholar · View at Scopus
  24. T. Takagi and M. Sugeno, “Fuzzy identification of systems and its applications to modeling and control,” IEEE Transactions on Systems, Man and Cybernetics, vol. 15, no. 1, pp. 116–132, 1985. View at Google Scholar · View at Scopus
  25. J. Sobhani and M. Najimi, “Numerical study on the feasibility of dynamic evolving neural-fuzzy inference system for approximation of compressive strength of dry-cast concrete,” Applied Soft Computing Journal, vol. 24, pp. 572–584, 2014. View at Publisher · View at Google Scholar · View at Scopus
  26. M. Buragohain and C. Mahanta, “A novel approach for ANFIS modelling based on full factorial design,” Applied Soft Computing, vol. 8, no. 1, pp. 609–625, 2008. View at Publisher · View at Google Scholar · View at Scopus
  27. F.-J. Chang and Y.-T. Chang, “Adaptive neuro-fuzzy inference system for prediction of water level in reservoir,” Advances in Water Resources, vol. 29, no. 1, pp. 1–10, 2006. View at Publisher · View at Google Scholar · View at Scopus
  28. J. Jantzen, “Neurofuzzy Modelling,” Tech. Rep., Technical University of Denmark, Department of Automation, Denmark, 1998. View at Google Scholar
  29. S. Heddam and N. Dechemi, “A new approach based on the dynamic evolving neural-fuzzy inference system (DENFIS) for modelling coagulant dosage (Dos): case study of water treatment plant of Algeria,” Desalination and Water Treatment, vol. 53, no. 4, pp. 1045–1053, 2015. View at Publisher · View at Google Scholar · View at Scopus
  30. O. Kisi and H. Sanikhani, “Modelling long-term monthly temperatures by several data-driven methods using geographical inputs,” International Journal of Climatology, vol. 35, no. 13, pp. 3834–3846, 2015. View at Publisher · View at Google Scholar · View at Scopus
  31. O. Kisi, “Pan evaporation modeling using least square support vector machine, multivariate adaptive regression splines and M5 model tree,” Journal of Hydrology, vol. 528, pp. 312–320, 2015. View at Publisher · View at Google Scholar · View at Scopus