IJE TRANSACTIONS A: Basics Vol. 22, No. 4 (November 2009) 351-358   

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A. Goel

Department of Civil Engineering, National Institute of Technology, Deemed University
Kurukshetra 136119, Haryana, India
( Received: October 16, 2008 – Accepted in Revised Form: July 02, 2009 )

Abstract    This paper is an attempt to assess the potential and usefulness of ANN based modeling for evaporation prediction from a reservoir, where in classical and empirical equations failed to predict the evaporation accurately. The meteorological data set of daily pan evaporation, temperature, solar radiation, relative humidity, wind speed is used in this study. The performance of feed forward back propagation neural network model is compared with the linear regression on the basis of performance parameters (correlation coefficient and rmse) having different combinations of input parameters. The comparison of results shows that there is a better agreement when large input parameters are considered for model building and testing as compared to a single parameter. The outcome of study suggests that the feed forward back propagation ANN based modeling can be applied as an alternative approach for estimation of daily evaporation from reservoirs effectively.


Keywords    Linear Regression, Neural Network, Correlation Coefficient, Evaporation


چکیده    در اين مقاله تلاش شده تا امکان و قابليت هاي مدل سازي بر پايه ANN به منظور پيش بيني تبخير از يک مخزن در حالتي که معادلات کلاسيک و تجربي نتوانسته اند تبخير را به طور دقيق پيش بيني کنند، سنجيده شود. يک دسته داده هاي هواشناسي شامل تبخير سطحي روزانه، دما، تشعشع خورشيدي، رطوبت نسبي و سرعت باد در اين مطالعه استفاده شده است. ترکيب هاي مختلفي از پارامترها به عنوان ورودي اعمال مي شود و به ازاي آن عملکرد پس انتشار فيد فوروارد در مدل شبکه عصبي با بازگشت خطي بر اساس پارامترهاي عملکرد (ضريب همبستگي و rmse) مقايسه مي گردد. مقايسه نتايج نشان مي دهد که وقتي براي ساخت و آزمايشِ مدل، پارامترهاي ورودي، بيشتر در نظر گرفته مي شوند، در مقايسه با حالت تک پارامتري هم خواني بهتري مشاهده مي شود. اين مطالعه نشان مي دهد که مدل سازي پس انتشار فيد فوروارد بر پايه ANN مي تواند به عنوان يک رويکرد جايگزين و کارآ براي تخمين تبخير روزانه از مخازن اعمال گردد.


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