Daily pan evaporation modelling using a neuro-fuzzy computing technique

Summary Evaporation, as a major component of the hydrologic cycle, is important in water resources development and management. This paper investigates the abilities of neuro-fuzzy (NF) technique to improve the accuracy of daily evaporation estimation. Five different NF models comprising various combinations of daily climatic variables, that is, air temperature, solar radiation, wind speed, pressure and humidity are developed to evaluate degree of effect of each of these variables on evaporation. A comparison is made between the estimates provided by the NF model and the artificial neural networks (ANNs). The Stephens–Stewart (SS) method is also considered for the comparison. Various statistic measures are used to evaluate the performance of the models. Based on the comparisons, it was found that the NF computing technique could be employed successfully in modelling evaporation process from the available climatic data. The ANN also found to perform better than the SS method.

[1]  S. Supharatid.  Application of a neural network model in establishing a stage–discharge relationship for a tidal river , 2003 .

[2]  Ozgur Kisi,et al.  River Flow Modeling Using Artificial Neural Networks , 2004 .

[3]  Edward T. Linacre,et al.  Climate and the Evaporation from Crops , 1967 .

[4]  Richard P. Lippmann,et al.  An introduction to computing with neural nets , 1987 .

[5]  Discussion of “Estimating Actual Evapotranspiration from Limited Climatic Data Using Neural Computing Technique” by K. P. Sudheer, A. K. Gosain, and K. S. Ramasastri , 2005 .

[6]  Clayton L. Hanson.  Prediction of Class A Pan Evaporation in Southwest Idaho , 1989 .

[7]  Dorota Z. Haman,et al.  Evaluation of Class A Pan Coefficients for Estimating Reference Evapotranspiration in Humid Location , 2002 .

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

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

[10]  O. Kisi,et al.  Discussion of “Forecasting of Reference Evapotranspiration by Artificial Neural Networks” by Slavisa Trajkovic, Branimir Todorovic, and Miomir Stankovic , 2005 .

[11]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[12]  Nachimuthu Karunanithi,et al.  Neural Networks for River Flow Prediction , 1994 .

[13]  Özgür Kişi,et al.  Daily River Flow Forecasting Using Artificial Neural Networks and Auto-Regressive Models , 2005 .

[14]  null null,et al.  Artificial Neural Networks in Hydrology. II: Hydrologic Applications , 2000 .

[15]  Samuel O. Russell,et al.  Reservoir Operating Rules with Fuzzy Programming , 1996 .

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

[17]  E. Mizutani,et al.  Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.

[18]  G. Tayfur.  Artificial neural networks for sheet sediment transport , 2002 .

[19]  Robert D. Burman.  Intercontinental Comparison of Evaporation Estimates , 1976 .

[20]  Robert W. Hill,et al.  Estimation of FAO Evapotranspiration Coefficients , 1983 .

[21]  O. Ks.  Multi-layer perceptrons with Levenberg-Marquardt training algorithm for suspended sediment concentration prediction and estimation , 2004 .

[22]  T. Sathish,et al.  River Flow Forecasting using Recurrent Neural Networks , 2004 .

[23]  Özgür Kişi,et al.  Multi-layer perceptrons with Levenberg-Marquardt training algorithm for suspended sediment concentration prediction and estimation / Prévision et estimation de la concentration en matières en suspension avec des perceptrons multi-couches et l’algorithme d’apprentissage de Levenberg-Marquardt , 2004 .

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

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

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

[27]  Ozgur Kisi,et al.  Suspended sediment estimation using neuro-fuzzy and neural network approaches/Estimation des matières en suspension par des approches neurofloues et à base de réseau de neurones , 2005 .

[28]  W. Brutsaert.  Evaporation into the atmosphere , 1982 .

[29]  Alessandro Ancarani,et al.  A Neural Networks Approach for Deriving Irrigation Reservoir Operating Rules , 2002 .

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

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

[32]  Ozgur Kisi,et al.  River suspended sediment modelling using a fuzzy logic approach , 2006 .

[33]  Ozgur Kisi,et al.  Flow prediction by three back propagation techniques using k-fold partitioning of neural network training data , 2005 .

[34]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[35]  Philipp Slusallek,et al.  Introduction to real-time ray tracing , 2005, SIGGRAPH Courses.

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

[37]  Ozgur Kisi,et al.  Methods to improve the neural network performance in suspended sediment estimation , 2006 .