Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Water Resources Management
Published in: Water Resources Management 15/2012

01-12-2012

Estimation of Daily Pan Evaporation Using Two Different Adaptive Neuro-Fuzzy Computing Techniques

Authors: Hadi Sanikhani, Ozgur Kisi, Mohammad Reza Nikpour, Yagob Dinpashoh

Published in: Water Resources Management | Issue 15/2012

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

This paper investigates the ability of two different adaptive neuro-fuzzy inference systems (ANFIS) including grid partitioning (GP) and subtractive clustering (SC), in modeling daily pan evaporation (Epan). The daily climatic variables, air temperature, wind speed, solar radiation and relative humidity of two automated weather stations, San Francisco and San Diego, in California State are used for pan evaporation estimation. The results of ANFIS-GP and ANFIS-SC models are compared with multivariate non-linear regression (MNLR), artificial neural network (ANN), Stephens-Stewart (SS) and Penman models. Determination coefficient (R2), root mean square error (RMSE) and mean absolute relative error (MARE) are used to evaluate the performance of the applied models. Comparison of results indicates that both ANFIS-GP and ANFIS-SC are superior to the MNLR, ANN, SS and Penman in modeling Epan. The results also show that the difference between the performances of ANFIS-GP and ANFIS-SC is not significant in evaporation estimation. It is found that two different ANFIS models could be employed successfully in modeling evaporation from available climatic data.
previous article A Systematic Review of Water Vulnerability Assessment Tools
next article Regional-Specific Numerical Models of Evapotranspiration Using Gene-Expression Programming Interface in Sahel

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now
Literature
Abonyi J, Andersen H, Nagy L, Szeifert F (1999) Inverse fuzzy-process-model based direct adaptive control. Math Comput Simul 51:119–132CrossRef
Al-Shalan A, Salih AMA (1987) Evapotranspiration estimation in extremely arid areas. ASCE J Irrig Drain Eng 113:565–574CrossRef
Aytek A (2009) Co-active neurofuzzy inference system for evapotranspiration modeling. Soft Comput 13:691–700CrossRef
Chiu S (1994) Fuzzy model identification based on cluster estimation. J Intell Fuzzy Syst 2(3):267–278
Chiu S (1997) Extracting fuzzy rules from data for function approximation and pattern classification. In: Dubois D, Prade H, Yager R (eds) Fuzzy information engineering: A Guided Tour of Applications. Springer, Berlin, pp 149–162
Chu HJ, Chang LC (2009) Application of optimal control and fuzzy theory for dynamic groundwater remediation design. Water Resour Manag 23(4):647–660CrossRef
Cobaner M (2011) Evapotranspiration estimation by two different neuro-fuzzy inference systems. J Hydrol 398:292–302CrossRef
Coulomb CV, Legesse D, Gasse F, Travi Y, Chernet T (2001) Lake evaporation estimates in tropical Africa (Lake Ziway, Ethiopia). J Hydrol 245(1–4):1–18CrossRef
Dogan E, Gumrukcuoglu M, Sandalci M, Opan M (2010) Modelling of evaporation from the reservoir of Yuvacik dam using adaptive neuro-fuzzy inference systems. Eng Appl Artif Intell 23:961–967CrossRef
Drake JT (2000) Communications phase synchronization using the adaptive network fuzzy inference system. Dissertation, New Mexico State University
El-Shafie A, Taha MR, Noureldin A (2007) A neuro-fuzzy model for inflow forecasting of the Nile river at Aswan high dam. Water Resour Manag 21:533–556CrossRef
Gaven H, Agnew CA (2004) Modelling actual, reference and equilibrium evaporation from temperate wet grassland. Hydrol Process 18(2):229–246CrossRef
Goyal MK, Ojha CSP (2011) Estimation of scour downstream of a ski-jump bucket using support vector and M5 model tree. Water Resour Manag 25(9):2177–2195CrossRef
Guldal V, Tongal H (2010) Comparison of recurrent neural network, adaptive neuro-fuzzy inference system and stochastic models in Egirdir Lake level forecasting. Water Resour Manag 24(1):105–128CrossRef
Guven A, Kisi O (2011) Daily pan evaporation modeling using linear genetic programming technique. Irrig Sci 29(2):135–145CrossRef
Jackson RD (1985) Evaluating evapotranspiration at local and regional scales. Proc IEEE 73(6):1086–1096CrossRef
Jang JSR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685CrossRef
Keskin ME, Terzi O (2006) Artificial neural network models of daily pan evaporation. J Hydrol Eng 11(1):65–70CrossRef
Keskin ME, Terzi O, Taylan D (2004) Fuzzy logic model approaches to daily pan evaporation estimation in Western Turkey. Hydrol Sci J 49(6):1001–1010CrossRef
Keskin ME, Terzi O, Taylan C (2009) Estimating daily pan evaporation using adaptive neural-based fuzzy inference system. Theor Appl Climatol 98:79–87CrossRef
Khu ST, Liong SY, Babovic V, Madsen H, Muttil N (2001) Genetic programming and its application in real-time runoff forming. J Am Water Resour Assoc 37(2):439–451CrossRef
Kisi O (2006a) Evapotranspiration estimation using feed forward neural networks. Nord Hydrol 37(3):247–260CrossRef
Kisi O (2006b) Daily pan evaporation modeling using a neuro-fuzzy computing technique. J Hydrol 329:636–646CrossRef
Kisi O (2007a) Evapotranspiration modeling from climate data using a neural computing technique. Hydrol Process 21(6):1925–1934CrossRef
Kisi O (2007b) Streamflow forecasting using different artificial neural network algorithms. ASCE J Hydrol Eng 12(5):532–539CrossRef
Kisi O (2009a) Daily pan evaporation modeling using multi-layer perceptrons and radial basis neural networks. Hydrol Process 23:213–223CrossRef
Kisi O (2009b) Modelling monthly evaporation using two different neural computing techniques. Irrig Sci 27(5):417–430CrossRef
Kisi O, Uncuoglu E (2005) Comparison of three backpropagation training algorithms for two case studies. Indian J Eng Mater Sci 12:443–450
Kisi O, Haktanir T, Ardiclioglu M, Ozturk O, Yalcin E, Uludag S (2009) Adaptive neuro-fuzzy computing technique for suspended sediment estimation. Adv Eng Softw 40:438–444CrossRef
Legates DR, McCabe GJ (1999) Evaluating the use of goodness-of-fit measures in hydrologic and hydroclimatic model validation. Water Resour Res 35(1):233–241CrossRef
Mamdani EH, Assilian S (1975) An experimental in linguistic synthesis with fuzzy logic controller. Int J Man–Mach Stud 7:1–13CrossRef
McCuen RH (1998) Hydrologic analysis and design. Prentice Hall, Englewood Cliffs
Moghadamnia A, Gousheh MG, Piri J, Amin S, Han D (2009) Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques. Adv Water Resour 32:88–97CrossRef
Penman WR (1948) Natural evaporation from open water, bare soil and grass. Proc R Soc Edinb A 193:120–145
Piri J, Amin S, Moghaddamnia A, Keshavarz A, Han D, Remesan R (2009) Daily Pan evaporation modeling in a hot and dry climate. J Hydrol Eng 14:803–812CrossRef
Rahimikhoob A (2008) Artificial neural network estimation of reference evapotranspiration from pan evaporation in a semi-arid environment. Irrig Sci 27:35–39CrossRef
Rahimikhoob A (2009) An evaluation of common pan coefficient equations to estimate reference evapotranspiration in a subtropical climate (north of Iran). Irrig Sci 27(4):289–296CrossRef
Rawlings JO (1988) Applied regression analysis: A research tool. Wadsworth & Brooks/Cole Advanced Books & Software, Pacific Grove, California
Razi MA, Athappilly K (2005) A comparative predictive analysis of neural networks (NNs), nonlinear regression and classification and regression tree (CART) models. Expert Syst Appl 29:65–74CrossRef
Reis RJ, Dias NL (1998) Multi-season lake evaporation: energy-budget estimates and CRLE model assessment with limited meteorological observations. J Hydrol 208(3–4):135–147CrossRef
Sabziparvar A-A, 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 Manag 24(5):909–920CrossRef
Sayed T, Tavakolie A, Razavi A (2003) Comparison of adaptive network based fuzzy inference systems and b-spline neuro-fuzzy mode choice models. J Comput Civ Eng 17(2):123–130CrossRef
Snyder RL (1993) Equation for evaporation pan to evapotranspiration conversions. J Irrig Drain Eng 118(6):977–980CrossRef
Sudheer KP, Gosain AK, Rangan DM, Saheb SM (2002) Modelling evaporation using an artificial neural network algorithm. Hydrol Process 16:3189–3202CrossRef
Tabari H, Marofi S, Sabziparvar AA (2010) Estimation of daily pan evaporation using artificial neural network and multivariate non-linear regression. Irrig Sci 28(5):399–406CrossRef
Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern 15:116–132CrossRef
Tan SBK, Shuy EB, Chua LHC (2007) Modelling hourly and daily open-water evaporation rates in areas with an equatorial climate. Hydrol Process 21(4):486–499CrossRef
Trajkovic S (2009) Comparison of radial basis function networks and empirical equations for converting from pan evaporation to reference evapotranspiration. Hydrol Process 23(6):874–880CrossRef
Trajkovic S (2010) Testing hourly reference evapotranspiration approaches using lysimeter measurements in a semiarid climate. Hydrol Res 41(1):38–49CrossRef
Trajkovic S, Kolakovic S (2010) Comparison of simplified pan-based equations for estimating reference evapotranspiration. J Irrig Drain Eng 136(2):137–140CrossRef
Trajkovic S, Stankovic M, Todorovic B (2000) Estimation of FAO Blaney-Criddle b Factor by RBF Network. J Irrig Drain Eng 126(4):268–271CrossRef
Traore S, Guven A (2011) New algebraic formulations of evapotranspiration extracted from gene-expression programming in the tropical seasonally dry regions of West Africa. Irrig Sci. doi:10.​1007/​s00271-011-0288-y
Traore S, Wang YM, Kerh T (2010) Artificial neural network for modeling reference evapotranspiration complex process in Sudano-Sahelian zone. Agric Water Manag 97:707–714CrossRef
Tsukamoto Y (1979) An approach to reasoning method. In: Gupta M, Ragade RK, Yager RR (eds) Adv Fuzzy Set Theo Appl, Amsterdam, p 137–149
Wang D, Singh V, Zhu Y (2007) Hybrid fuzzy and optimal modeling for water quality evaluation. Water Resour Res 43(5):W05415CrossRef
Wang D, Singh V, Zhu Y, Wu J (2009) Stochastic observation error and uncertainty in water quality evaluation. Adv Water Resour 32(10):1526–1534CrossRef
Wei M, Bai B, Sung AH, Liu Q, Wang J, Cather ME (2007) Predicting injection profiles using ANFIS. Inform Sci 177:4445–4461CrossRef
Yager RR, Filev DP (1994) Approximate clustering via the mountain method. IEEE Trans Syst Man Cybern 24(8):1279–1284CrossRef
Metadata
Title
Estimation of Daily Pan Evaporation Using Two Different Adaptive Neuro-Fuzzy Computing Techniques
Authors
Hadi Sanikhani
Ozgur Kisi
Mohammad Reza Nikpour
Yagob Dinpashoh
Publication date
01-12-2012
Publisher
Springer Netherlands
Published in
Water Resources Management / Issue 15/2012
Print ISSN: 0920-4741
Electronic ISSN: 1573-1650
DOI
https://doi.org/10.1007/s11269-012-0148-4

Other articles of this Issue 15/2012

Water Resources Management 15/2012 Go to the issue

OriginalPaper

Monthly Precipitation Forecasting with a Neuro-Fuzzy Model

OriginalPaper

A Comparative Evaluation of Short-Term Streamflow Forecasting Using Time Series Analysis and Rainfall-Runoff Models in eWater Source

OriginalPaper

Penalty-Free Feasibility Boundary Convergent Multi-Objective Evolutionary Algorithm for the Optimization of Water Distribution Systems

OriginalPaper

Evaluation of Vulnerability to Extreme Climatic Events in the Brazilian Amazonia: Methodological Proposal to the Rio Acre Basin

OriginalPaper

A Systematic Review of Water Vulnerability Assessment Tools

OriginalPaper

An Integration of Statistics Temporal Methods to Track the Effect of Drought in a Shallow Mediterranean Lake