Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
doi:10.5194/hess-2016-191
© Author(s) 2016. This work is distributed
under the Creative Commons Attribution 3.0 License.
Research article
13 Jun 2016
Review status
A revision of this discussion paper for further review has not been submitted.
A nonlinear modelling-based high-order response surface method for predicting monthly pan evaporations
Behrooz Keshtegar1 and Ozgur Kisi2 1Department of Civil Engineering, Faculty of Engineering, University of Zabol, P.B. 9861335856, Zabol, Iran
2Department of Civil Engineering, Faculty of Architecture and Engineering, University of Canik Basari, Samsun, Turkey
Abstract. Accurate modelling of pan evaporation has a vital importance in the planning and management of water resources. In this paper, the response surface method (RSM) is extended for estimation of monthly pan evaporations using high-order response surface (HORS) function. A HORS function is proposed to improve the accurate predictions with various climatic data, which are solar radiation, air temperature, relative humidity and wind speed from two stations, Antalya and Mersin, in Mediterranean Region of Turkey. The HORS predictions were compared to artificial neural networks (ANNs), neuro-fuzzy (ANFIS) and fuzzy genetic (FG) methods in these stations. Finally, the pan evaporation of Mersin station was estimated using input data of Antalya station in terms of HORS, FG, ANNs, and ANFIS modelling. Comparison results indicated that HORS models performed slightly better than FG, ANN and ANFIS models. The HORS approach could be successfully and simply applied to estimate the monthly pan evaporations.

Citation: Keshtegar, B. and Kisi, O.: A nonlinear modelling-based high-order response surface method for predicting monthly pan evaporations, Hydrol. Earth Syst. Sci. Discuss., doi:10.5194/hess-2016-191, in review, 2016.
Behrooz Keshtegar and Ozgur Kisi
Interactive discussionStatus: closed (peer review stopped)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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RC2: 'Review report hess-2016-191', Anonymous Referee #2, 26 Jul 2016 Printer-friendly Version 
Behrooz Keshtegar and Ozgur Kisi
Behrooz Keshtegar and Ozgur Kisi

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Short summary
the accurate and simple predictions of the hydrological events such as rain fall, evaporation and stream fallow are the important issues in the water resource management. in this paper a high-order response surface (HORS) method is proposed for prediction of pan evaporation. The estimates of HORS are compared with fuzzy genetic, neuro-fuzzy and ANN methods. It was found that the proposed model outperformed the soft computing methods. HORS model increased the RMSE accuracy of the FG.
the accurate and simple predictions of the hydrological events such as rain fall, evaporation...
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