The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON ENVIRONMENT AND DEVELOPMENT
Transactions ID Number: 89-574
Full Name: Alireza Moghaddamnia
Position: Assistant Professor
Age: ON
Sex: Male
Address: University of Zabol, Faculty of Natural Resources, Department of Range and Watershed Management, Zabol City, Iran
Country: IRAN
Tel: 00989151441381
Tel prefix:
Fax:
E-mail address: ali.moghaddamnia@gmail.com
Other E-mails: ali.moghaddamnia@live.com
Title of the Paper: Evaporation Estimation by Using Several Data Driven Techniques
Authors as they appear in the Paper: Alireza Moghaddamnia, Mosen Ghafari Gosheh, Mohammad Reza Jamalizadeh Tajababdi, Mehrdad Nuraie, Mohammad Alizadeh Mansuri and Dawei Han
Email addresses of all the authors: ali.moghaddamnia@gmail.com,mehrdad.nuraie@gmail.com
Number of paper pages: 10
Abstract: Estimating evaporation is an important issue for water resources management, planning water supplies, and irrigation systems. This article compares the accuracy of several data driven techniques, that is, Local Linear Regression (LLR), Support Vector Machine (SVM) and Artificial Neural Network (ANN) for estimating daily evaporation from reservoirs as one of the critical components of hydrological cycle in arid and semi-arid regions. A case study has been carried out in the Chahnimeh water reservoirs of Zabol located in the Sistan plain of Iran. Among the models used, in terms of the evaluation criteria of root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2), it is demonstrated that use of the nonlinear models of support vector machine (SVM), Broyden-Fletcher-Goldfarb-Shanno neural network (BFGSNN) and Conjugate Gradient neural network (CGNN) performed reasonably well in modeling the validation data compared to Local Linea!
r Regression (LLR) model but both BFGSNN and CGNN failed to reach the highest possible values. In the meantime, the SVM model was able to provide more reliable estimations compared to others.
Keywords: evaporation, data driven models, SVM, ANN, LLR, Chah Nimeh reservoirs of Iran
EXTENSION of the file: .rtf
Special (Invited) Session: Performance Evaluation of LLR, SVM, CGNN and BFGSNN Models to Evaporation Estimation
Organizer of the Session: 640-519
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