A nonlinear modelling-based high-order response surface method for predicting monthly pan evaporations
Behrooz Keshtegar1 and Ozgur Kisi21Department 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
Received: 23 Apr 2016 – Accepted for review: 10 Jun 2016 – Discussion started: 13 Jun 2016
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.
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.