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

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.

[1]  Chandra Shekhar P. Ojha,et al.  Estimation of Scour Downstream of a Ski-Jump Bucket Using Support Vector and M5 Model Tree , 2011 .

[2]  A. Rahimikhoob.  An evaluation of common pan coefficient equations to estimate reference evapotranspiration in a subtropical climate (north of Iran) , 2009, Irrigation Science.

[3]  Kuriakose Athappilly,et al.  A comparative predictive analysis of neural networks (NNs), nonlinear regression and classification and regression tree (CART) models , 2005, Expert Syst. Appl..

[4]  E. Uncuoğlu,et al.  Comparison of three backpropagation training algorithms for two case studies , 2010 .

[5]  Aytac Guven,et al.  Daily pan evaporation modeling using linear genetic programming technique , 2011, Irrigation Science.

[6]  Qingzhong Liu,et al.  Predicting injection profiles using ANFIS , 2007, Inf. Sci..

[7]  Dawei Han,et al.  Evaporation Estimation Using Artificial Neural Networks and Adaptive Neuro-Fuzzy Inference System Techniques , 2009 .

[8]  Shantilal Shah,et al.  Artificial neural network estimation of reference evapotranspiration from pan evaporation in a semi-arid environment , 2008, Irrigation Science.

[9]  Vijay P. Singh,et al.  Hybrid fuzzy and optimal modeling for water quality evaluation , 2007 .

[10]  Nelson Luís Dias,et al.  Multi-season lake evaporation: energy-budget estimates and CRLE model assessment with limited meteorological observations , 1998 .

[11]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[12]  Özlem Terzi,et al.  Artificial Neural Network Models of Daily Pan Evaporation , 2006 .

[13]  O. Kisi,et al.  Comparison of three back-propagation training algorithms for two case studies , 2005 .

[14]  Ozgur Kisi,et al.  Evapotranspiration modelling from climatic data using a neural computing technique , 2007 .

[15]  Stephen Boon Kean Tan,et al.  Modelling hourly and daily open‐water evaporation rates in areas with an equatorial climate , 2007 .

[16]  Laverne W. Stanton,et al.  Applied Regression Analysis: A Research Tool , 1990 .

[17]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[18]  M. Erol.  Fuzzy logic model approaches to daily pan evaporation estimation in western Turkey , 2005 .

[19]  R. Yager,et al.  Approximate Clustering Via the Mountain Method , 1994, IEEE Trans. Syst. Man Cybern. Syst..

[20]  D. Legates,et al.  Evaluating the use of “goodness‐of‐fit” Measures in hydrologic and hydroclimatic model validation , 1999 .

[21]  Ozgur Kisi,et al.  Daily pan evaporation modelling using a neuro-fuzzy computing technique , 2006 .

[22]  Liang-Cheng Chang,et al.  Application of Optimal Control and Fuzzy Theory for Dynamic Groundwater Remediation Design , 2009 .

[23]  Tienfuan Kerh,et al.  Artificial neural network for modeling reference evapotranspiration complex process in Sudano-Sahelian zone , 2010 .

[24]  Ozgur Kisi,et al.  Daily pan evaporation modelling using multi‐layer perceptrons and radial basis neural networks , 2009 .

[25]  Branimir Todorovic,et al.  Estimation of FAO Blaney-Criddle b Factor by RBF Network , 2000 .

[26]  Ozgur Kisi,et al.  Evapotranspiration estimation using feed-forward neural networks , 2006 .

[27]  Vijay P. Singh,et al.  Stochastic observation error and uncertainty in water quality evaluation , 2009 .

[28]  R. Jackson,et al.  Evaluating evapotranspiration at local and regional scales , 1985, Proceedings of the IEEE.

[29]  Slavisa Trajkovic,et al.  Comparison of simplified pan-based equations for estimating reference evapotranspiration. , 2010 .

[30]  R. McCuen.  Hydrologic Analysis and Design , 1997 .

[31]  Dawei Han,et al.  Closure to "Daily Pan Evaporation Modeling in a Hot and Dry Climate" , 2009 .

[32]  Özlem Terzi,et al.  Fuzzy Logic Model Approaches to Daily Pan Evaporation Estimation in Western Turkey , 2004 .

[33]  Didier Dubois,et al.  Fuzzy information engineering: a guided tour of applications , 1997 .

[34]  Ali Aytek,et al.  Co-active neurofuzzy inference system for evapotranspiration modeling , 2009, Soft Comput..

[35]  Slavisa Trajkovic,et al.  Testing hourly reference evapotranspiration approaches using lysimeter measurements in a semiarid climate. , 2010 .

[36]  Stephen L. Chiu,et al.  Fuzzy Model Identification Based on Cluster Estimation , 1994, J. Intell. Fuzzy Syst..

[37]  Nadipuram R. Prasad,et al.  Communications phase synchronization using the adaptive network fuzzy inference system (anfis) , 2000 .

[38]  Ferenc Szeifert,et al.  Inverse fuzzy-process-model based direct adaptive control , 1999 .

[39]  Ozgur Kisi,et al.  Streamflow Forecasting Using Different Artificial Neural Network Algorithms , 2007 .

[40]  K. P. Sudheer,et al.  Modelling evaporation using an artificial neural network algorithm , 2002 .

[41]  A. R. Khoob,et al.  Artificial neural network estimation of reference evapotranspiration from pan evaporation in a semi-arid environment , 2008, Irrigation Science.

[42]  Hakan Tongal,et al.  Comparison of Recurrent Neural Network, Adaptive Neuro-Fuzzy Inference System and Stochastic Models in Eğirdir Lake Level Forecasting , 2010 .

[43]  Richard L. Snyder,et al.  Equation for Evaporation Pan to Evapotranspiration Conversions , 1992 .

[44]  Stephen L. Chiu,et al.  Extracting Fuzzy Rules from Data for Function Approximation and Pattern Classification , 2000 .

[45]  H. L. Penman.  Natural evaporation from open water, bare soil and grass , 1948, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences.

[46]  Hossein Tabari,et al.  Evaluation of Class A Pan Coefficient Models for Estimation of Reference Crop Evapotranspiration in Cold Semi-Arid and Warm Arid Climates , 2010 .

[47]  Özgür Kisi,et al.  Adaptive neuro-fuzzy computing technique for suspended sediment estimation , 2009, Adv. Eng. Softw..

[48]  M. Cobaner.  Evapotranspiration estimation by two different neuro-fuzzy inference systems , 2011 .

[49]  A. Guven,et al.  New algebraic formulations of evapotranspiration extracted from gene-expression programming in the tropical seasonally dry regions of West Africa , 2011, Irrigation Science.

[50]  Özgür Kişi,et al.  Modeling monthly evaporation using two different neural computing techniques , 2009, Irrigation Science.

[51]  M. Keskin,et al.  Fuzzy logic model approaches to daily pan evaporation estimation in western Turkey / Estimation de l’évaporation journalière du bac dans l’Ouest de la Turquie par des modèles à base de logique floue , 2004 .

[52]  Ahmed El-Shafie,et al.  A neuro-fuzzy model for inflow forecasting of the Nile river at Aswan high dam , 2007 .

[53]  Vladan Babovic,et al.  GENETIC PROGRAMMING AND ITS APPLICATION IN REAL‐TIME RUNOFF FORECASTING 1 , 2001 .

[54]  Özlem Terzi,et al.  Estimating daily pan evaporation using adaptive neural-based fuzzy inference system , 2009 .

[55]  Y. Travi,et al.  Lake evaporation estimates in tropical Africa (Lake Ziway, Ethiopia) , 2001 .

[56]  Emrah Dogan,et al.  Modelling of evaporation from the reservoir of Yuvacik dam using adaptive neuro-fuzzy inference systems , 2010, Eng. Appl. Artif. Intell..

[57]  H. Gavin,et al.  Modelling actual, reference and equilibrium evaporation from a temperate wet grassland , 2004 .

[58]  Slavisa Trajkovic,et al.  Comparison of radial basis function networks and empirical equations for converting from pan evaporation to reference evapotranspiration , 2009 .

[59]  Hossein Tabari,et al.  Estimation of daily pan evaporation using artificial neural network and multivariate non-linear regression , 2010, Irrigation Science.

[60]  Wolfgang Banzhaf,et al.  Genetic Programming and Its Application in Machining Technology , 2003 .

[61]  Tarek Sayed,et al.  Comparison of Adaptive Network Based Fuzzy Inference Systems and B-spline Neuro-Fuzzy Mode Choice Models , 2003 .

[62]  Yahachiro Tsukamoto,et al.  AN APPROACH TO FUZZY REASONING METHOD , 1993 .

[63]  Solaiman A. Al-Sha’lan,et al.  Evapotranspiration Estimates in Extremely Arid Areas , 1987 .