Theoretical and Applied Climatology

, Volume 98, Issue 1–2, pp 79–87 | Cite as

Estimating daily pan evaporation using adaptive neural-based fuzzy inference system

Original Paper

Abstract

Estimation of evaporation is important for water planning, management, and hydrological practices. There are many available methods to estimate evaporation from a water surface, comprising both direct and indirect methods. All the evaporation models are based on crisp conceptions with no uncertainty element coupled into the model structure although in daily evaporation variations there are uncontrollable effects to a certain extent. The probabilistic, statistical, and stochastic approaches require large amounts of data for the modeling purposes and therefore are not practical in local evaporation studies. It is therefore necessary to adopt a better approach for evaporation modeling, which is the fuzzy sets and adaptive neural-based fuzzy inference system (ANFIS) as used in this paper. ANFIS and fuzzy sets have been evaluated for its applicability to estimate evaporation from meteorological data which is including air and water temperatures, solar radiation, and air pressure obtained from Automated GroWheather meteorological station located near Lake Eğirdir and daily pan evaporation values measured by XVIII. District Directorate of State Hydraulic Works. Results of ANFIS and fuzzy logic approaches were analyzed and compared with measured daily pan evaporation values. ANFIS approach could be employed more successfully in modeling the evaporation process than fuzzy sets.

References

  1. Abtew W (2001) Evaporation estimation for Lake Okeechobee in south Florida. J Irrig Drain Eng 127:140–147CrossRefGoogle Scholar
  2. Andersen ME, Jobson HE (1982) Comparison of techniques for estimating annual lake evaporation using climatological data. Water Resour Res 18(3):630–636CrossRefGoogle Scholar
  3. Aronica G, Hankin B, Beven K (1998) Uncertainty and equifinality in calibrating distributed roughness coefficients in a flood propagation model with limited data. Adv Water Resour 22(4):349–365CrossRefGoogle Scholar
  4. Bardossy A, Disse M (1993) Fuzzy rule based models for infiltration. Water Resour Res 29(2):373–382CrossRefGoogle Scholar
  5. Center B, Verma BP (1998) Fuzzy logic for biological and agricultural systems. Artif Intel Rev 12:213–225CrossRefGoogle Scholar
  6. Chang L-C, Chang F-J (2001) Intelligent control for modeling of real-time reservoir operation. Hydrologic Proc 15:1621–1634CrossRefGoogle Scholar
  7. Choudhury BJ (1999) Evaluation of an empirical equation for annual evaporation using field observations and results from a biophysical model. J Hydrol 216:99–110CrossRefGoogle Scholar
  8. de Bruin HAR (1978) A simple model for shallow lake evaporation. J Appl Met 17:1132–1134CrossRefGoogle Scholar
  9. Dubrovin T, Jolma A, Turunen E (2002) Fuzzy model for real-time reservoir operation. J Water Resour Plann Manage 128(1):66–73CrossRefGoogle Scholar
  10. Jang JSR (1992) Self-learning fuzzy controllers based on temporal back propagation. IEEE Trans Neural Networks 3(5): 714–723CrossRefGoogle Scholar
  11. Keskin ME, Terzi O, Taylan D (2004) Fuzzy logic model approaches to daily pan evaporation estimation in western Turkey. Hydrologic Sci J 49(6):1001–1010CrossRefGoogle Scholar
  12. Keskin ME, Terzi O, Taylan D (2005) Reply to Discussion of “Fuzzy logic model approaches to daily pan evaporation estimation in western Turkey”. Hydrologic Sci J 50(4):729–730Google Scholar
  13. Kindler J (1992) Rationalizing water requirements with aid of fuzzy allocation model. J Water Resour Plann Manage 118(3):308–323CrossRefGoogle Scholar
  14. Kisi O (2005) Discussion of “Fuzzy logic model approaches to daily pan evaporation estimation in western Turkey”. Hydrologic Sci J 50(4):729–730Google Scholar
  15. Lin CT, Lee CSG (1995) Neural fuzzy systems. Prentice Hall, Upper Saddle River, NJ, p 797Google Scholar
  16. Linsley RK, Kohler MA, Paulhus JLH (1982) Hydrology for engineers. McGraw-Hill, LondonGoogle Scholar
  17. Mahabir C, Hicks FE, Robinson-Fayek A (2003) Application of fuzzy logic to forecast seasonal runoff. Hydrologic Process 17:3749–3762CrossRefGoogle Scholar
  18. McKenzie RS, Craig AR (2001) Evaluation of river losses from the Orange River using hydraulic modeling. J Hydrol 241:62–69CrossRefGoogle Scholar
  19. Morton FI (1979) Climatological estimates of lake evaporation. Water Resour Res 15:64–76CrossRefGoogle Scholar
  20. Nayak PC, Sudheer KP, Rangan DM, Ramasastri KS (2004) A neuro-fuzzy computing technique for modeling hydrological time series. J Hydrol 291(1–2):52–66CrossRefGoogle Scholar
  21. Pongracz R, Bogardi I, Duckstein L (1999) Application of fuzzy rule-based modeling technique to regional drought. J Hydrol 224:100–114CrossRefGoogle Scholar
  22. Russell SO, Cambell PE (1996) Reservoir operating rules with fuzzy logic programming. J Water Resour Plann Manage 122(4):262–269CrossRefGoogle Scholar
  23. Stewart RB, Rouse WR (1976) A simple method for determining the evaporation from shallow lakes and ponds. Water Resour Res 12:623–627CrossRefGoogle Scholar
  24. Şen Z (1998) Fuzzy algorithm for estimation irradiation from sunshine duration. Solar Energy 63(1):39–49CrossRefGoogle Scholar
  25. Tayfur G, Özdemir S, Singh VP (2003) Fuzzy logic algorithm for runoff-induced sediment transport from bare soil surfaces. Adv Water Resour 26:1249–1256CrossRefGoogle Scholar
  26. Tsoukalas LH, Uhrig RE (1997) Fuzzy and neural approaches in engineering. A Wiley-Interscience Publications, Wiley, New York, p 587Google Scholar
  27. Vallet-Coulomb C, Legesse D, Gasse F, Travi Y, Chernet T (2001) Lake evaporation estimates in tropical Africa (Lake Ziway, Ethiopia). J Hydrol 245:1–18CrossRefGoogle Scholar
  28. Warnaka K, Pochop L (1988) Analyses of equation for free water evaporation estimates. Water Resour Res 24(7):979–984CrossRefGoogle Scholar
  29. Zadeh LA (1965) Fuzzy sets. Inform Control 8:338–353CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  1. 1.Faculty of Engineering-ArchitectureSuleyman Demirel UniversityIspartaTurkey
  2. 2.Technical Education FacultySuleyman Demirel UniversityIspartaTurkey

Personalised recommendations