Evaluation of the Performance of Artificial Neural Network and Support Vector Machine Models in Estimation of Daily Evaporation amounts (Case study: Tabriz and Maragheh Synoptic Stations)

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ISAZADEH MOHAMMAD; SHIRZAD MONIR; REZAEI BANAFSHEH MAJID

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Journal: Physical Geography Research Year:2017 | Volume:49 | Issue:1 Page(s): 25-27

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Title

Evaluation of the Performance of Artificial Neural Network and Support Vector Machine Models in Estimation of Daily Evaporation amounts (Case study: Tabriz and Maragheh Synoptic Stations)

Author(s)

ISAZADEH MOHAMMAD | SHIRZAD MONIR | REZAEI BANAFSHEH MAJID | Issue Writer Certificate

Keywords

artificial neural networkQ2

evaporation estimationQ1

MaraghehQ1

Support Vector MachineQ1

TabrizQ1

Abstract

Introduction Evaporation is a fundamental component of the hydrology cycle and has an important role in water resources management. Daily evaporation is an important variable in reservoir capacity, rainfall-runoff modeling, crop management and water balance. Measurementof actual evaporation is almost impossible, but evaporation can be estimated using several methods. There are two general viewpoints for estimation of evaporation: direct and indirect methods. It is inoperative to measurement of the evaporation by direct methods in all locations. The direct methods are usually used for proximate reservoirs or irrigation projects. The indirect methods of evaporation estimation need various input data that are not easily available. Moreover, the evaporation have very complex and nonlinear process that simulation of its complex process using simple methods is impractical. In recent years, the artificial intelligent methods such as artificial neural network (ANN) and Support Vector Machine (SVM) have been successfully utilized for modeling the hydrological nonlinear process such as rainfall, precipitation, rainfall-runoff, evaporation, temperature, water quality, stream flow, water level and suspended sediment, etc. Therefore, this research evaluates the performance of ANN and SVM models in daily evaporation estimation.

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APA: Copy

ISAZADEH, MOHAMMAD, SHIRZAD, MONIR, & REZAEI BANAFSHEH, MAJID. (2017). Evaluation of the Performance of Artificial Neural Network and Support Vector Machine Models in Estimation of Daily Evaporation amounts (Case study: Tabriz and Maragheh Synoptic Stations). PHYSICAL GEOGRAPHY RESEARCH QUARTERLY, 49(1 ), 25-27. SID. https://sid.ir/paper/369217/en

Vancouver: Copy

ISAZADEH MOHAMMAD, SHIRZAD MONIR, REZAEI BANAFSHEH MAJID. Evaluation of the Performance of Artificial Neural Network and Support Vector Machine Models in Estimation of Daily Evaporation amounts (Case study: Tabriz and Maragheh Synoptic Stations). PHYSICAL GEOGRAPHY RESEARCH QUARTERLY[Internet]. 2017;49(1 ):25-27. Available from: https://sid.ir/paper/369217/en

IEEE: Copy

MOHAMMAD ISAZADEH, MONIR SHIRZAD, and MAJID REZAEI BANAFSHEH, “Evaluation of the Performance of Artificial Neural Network and Support Vector Machine Models in Estimation of Daily Evaporation amounts (Case study: Tabriz and Maragheh Synoptic Stations),” PHYSICAL GEOGRAPHY RESEARCH QUARTERLY, vol. 49, no. 1 , pp. 25–27, 2017, [Online]. Available: https://sid.ir/paper/369217/en

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