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Neural Computing and Applications

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01.09.2013 | Original Article

Daily pan evaporation estimation using gene expression programming and adaptive neural-based fuzzy inference system

verfasst von: Özlem Terzi

Erschienen in: Neural Computing and Applications | Ausgabe 3-4/2013

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Abstract

This study was conducted using gene expression programming (GEP) and an adaptive neural-based fuzzy inference system (ANFIS) as an alternative approach to estimate daily pan evaporation, which is an important parameter in hydrological and meteorological studies. The input parameters used to estimate daily pan evaporation from Lake Eğirdir in the southwestern part of Turkey are the daily pan evaporation data of Lake Kovada (Ko_t) and Lake Karacaören Dam (Ka_t) and the previous 1-, 2-, and 3-day pan evaporation values of Lake Eğirdir. The various input combinations were tried by using pan evaporation data for the years 1998–2005. The GEP model with the highest Nash–Sutcliffe efficiency and the lowest mean square error have the daily pan evaporation data of Lake Kovada (Ko_t) and Lake Karacaören Dam (Ka_t) and the previous 1-day pan evaporation values of Lake Eğirdir. The NSE of the best GEP model was obtained as 0.729, 0.722, and 0.701 for training, testing, and validation sets, respectively. Furthermore, the ANFIS models were developed using the same input combinations. It was seen that the GEP model was more superior to the ANFIS model.

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Abtew W (2001) Evaporation estimation for Lake Okeechobee in South Florida. J Irrig Drain Eng 127(3):140–147CrossRef

Bazarseren B, Hildebrandt G, Holz KP (2003) Short-term water level prediction using neural networks and neuro-fuzzy approach. Neurocomputing 55(3–4):439–450CrossRef

Chang CL, Lo SL, Yu SL (2005) Applying fuzzy theory and genetic algorithm to interpolate precipitation. J Hydrol 314:92–104CrossRef

Chang L-C, Chang F-J (2001) Intelligent control for modeling of real-time reservoir operation. Hydrol Process 15:1621–1634CrossRef

Cheng CT, Ou CP, Chau KW (2002) Combining a fuzzy optimal model with a genetic algorithm to solve multi-objective rainfall–runoff model calibration. J Hydrol 268:72–86CrossRef

Choudhury BJ (1999) Evaluation of an empirical equation for annual evaporation using field observations and results from a biophysical model. J Hydrol 216:99–110CrossRef

Ferreira C (2001) Gene expression programming: a new adaptive algorithm for solving problems. Complex Syst 13(2):87–129MATH

Ferreira C (2002) Gene expression programming in problem solving. In: Roy R, Ovaska S, Furuhashi T, Hoffman F (eds) Soft computing and industry-recent applications. Springer, Berlin, pp 635–654

Ferreira C (2006) Gene-expression programming: mathematical modeling by an artificial intelligence. Springer, Berlin

10.

Fırat M, Güngör M (2007) River flow estimation using adaptive neuro fuzzy inference system. Math Comput Simul 75:87–96CrossRefMATH

11.

Frankenberger JR, Brooks ES, Walter MT, Walter MF, Steenhuis TS (1999) A GIS-based variable source area hydrology model. Hydrol Process 13:805–822CrossRef

12.

Ghorbani MA, Khatibi R, Aytek A, Makarynskyy O, Shiri J (2010) Sea water level forecasting using genetic programming and comparing the performance with artificial neural networks. Comput Geosci 36:620–627CrossRef

13.

Gupta HV, Kling H, Yılmaz KK, Martinez GF (2009) Decomposition of the mean squared error and NSE performance criteria: implications for improving hydrological modeling. J Hydrol 377:80–91CrossRef

14.

Güven A, Aytek A (2009) New approach for stage–discharge relationship: gene-expression programming. J Hydrol Eng 14(8):812–820CrossRef

15.

Güven A, Kişi Ö (2011) Daily pan evaporation modeling using linear genetic programming technique. Irrig Sci 29:135–145CrossRef

16.

Ikebuchi S, Seki M, Ohtoh A (1988) Evaporation from Lake Biwa. J Hydrol 102:427–449CrossRef

17.

Jang JSR (1992) Self-learning fuzzy controllers based on temporal back propagation. IEEE Trans Neural Netw 3(5):714–723CrossRef

18.

Keskin ME, Taylan ED (2009) Artificial models for inter-basin flow prediction in southern Turkey. J Hydrol Eng 14(7):752–758CrossRef

19.

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

20.

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

21.

Keskin ME, Terzi Ö, Taylan D (2009) Estimating daily pan evaporation using adaptive neural-based fuzzy inference system. Theor Appl Climatol 98(1–2):79–87CrossRef

22.

Keskin ME, Terzi Ö, Küçüksille E (2009) Data mining process for integrated evaporation model. J Irrig Drain Eng 135(1):39–43CrossRef

23.

Kişi Ö (2006) Daily pan evaporation modelling using a neuro-fuzzy computing technique. J Hydrol 329:636–646CrossRef

24.

Krause P, Boyle DP, Base F (2005) Comparison of different efficiency criteria for hydrological model assessment. Adv Geosci 5:89–97CrossRef

25.

Koza JR (1992) Genetic programming on the programming of computers by means of natural selection. A Bradford book. The MIT Press, Cambridge

26.

Lin CT, Lee CSG (1995) Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems. Prentice Hall, Englewood Cliffs, p 797

27.

Makkeasorn A, Chang NB, Zhou X (2008) Short-term streamflow forecasting with global climate change implications—a comparative study between genetic programming and neural network models. J Hydrol 352:336–354CrossRef

28.

McCuen RH, Knight Z, Cutter AG (2006) Evaluation of the Nash-Sutcliffe efficiency. J Hydrol Eng 11(6):597–602CrossRef

29.

McKenzie RS, Craig AR (2001) Evaluation of river losses from the Orange River using hydraulic modeling. J Hydrol 241:62–69CrossRef

30.

Murphy A (1988) Skill scores based on the mean square error and their relationships to the correlation coefficient. Mon Weather Rev 116:2417–2424CrossRef

31.

Nash JE, Sutcliffe JV (1970) River flow forecasting through. Part I. A conceptual models discussion of principles. J Hydrol 10:282–290CrossRef

32.

Nayak PC, Sudheer KP, Rangan DM, Ramasastr KS (2004) A neuro-fuzzy computing technique for modeling hydrological time series. J Hydrol 291(1–2):52–66CrossRef

33.

Ponce VM (1989) Engineering hydrology, principles and practices. Prentice-Hall, New Jersey

34.

Rao HS, Babu BR (2006) Optimized column design using genetic algorithm based neural networks. Indian J Eng Mater Sci 13:503–511

35.

Reddy MJ, Ghimire BNS (2009) Use of model tree and gene expression programming to predict the suspended sediment load in rivers. J Intell Syst 18(3):211–228

36.

Saltan M, Terzi S (2005) Comparative analysis of using artificial neural networks (ANN) and gene expression programming (GEP) in backcalculation of pavement layer thickness. Indian J Eng Mater Sci 12:42–50

37.

Savic DA, Walters GA, Davidson JW (1999) A genetic programming approach to rainfall-runoff modelling. Water Resour Manag 13:219–231CrossRef

38.

Shu C, Ouarda TBMJ (2008) Regional flood frequency analysis at ungauged sites using the adaptive neuro-fuzzy inference system. J Hydrol 349(1–2):31–43CrossRef

39.

Singh VP, Xu C-Y (1997) Evaluation and generalization of 13 mass-transfer equations for determining free water evaporation. Hydrol Process 11:311–323CrossRef

40.

Singh VP, Xu C-Y (1997) Sensitivity of mass transfer-based evaporation equations to errors in daily and monthly input data. Hydrol Process 11:1465–1473CrossRef

41.

Teegavarapu RSV, Tufail M, Ormsbee L (2009) Optimal functional forms for estimation of missing precipitation data. J Hydrol 374:106–115CrossRef

42.

Terzi Ö, Keskin ME, Taylan D (2006) Estimating evaporation using ANFIS. J Irrig Drain Eng 132(5):503–507CrossRef

43.

Tsoukalas LH, Uhrig RE (1997) Fuzzy and neural approaches in engineering. A Wiley-Interscience Publications. Wiley, New York, p 587

44.

Vallet-Coulomb C, Legesse D, Gasse F, Travi Y, Chernet T (2001) Lake evaporation estimates in tropical Africa (Lake Ziway, Ethiopia). J Hydrol 245:1–18CrossRef

45.

Whigham PA, Crapper PF (2001) Modelling rainfall-runoff using genetic programming. Math Comput Model 33:707–721CrossRefMATH

46.

Xu C-Y, Singh VP (2001) Evaluation and generalization of temperature-based methods for calculating evaporation. Hydrol Process 15:305–319CrossRef

47.

Zhang FX, Wai WHO, Jiang YW (2010) Prediction of sediment transportation in deep bay (Hong Kong) using genetic algorithm. J Hydrodyn 22(5):599–604CrossRef

Titel: Daily pan evaporation estimation using gene expression programming and adaptive neural-based fuzzy inference system
verfasst von: Özlem Terzi
Publikationsdatum: 01.09.2013
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 3-4/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-012-1027-x

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