Modeling monthly pan evaporation using wavelet support vector regression and wavelet artificial neural networks in arid and humid climates

Sultan Noman Qasem,Saeed Samadianfard,Salar Kheshtgar,Salar Jarhan,Ozgur Kisi,Shahaboddin Shamshirband,Kwok-Wing Chau

doi:10.1080/19942060.2018.1564702

Abstract

ABSTRACTEvaporation rate is one of the key parameters in determining the ecological conditions and it has an irrefutable role in the proper management of water resources. In this paper, the efficiency of some data-driven techniques including support vector regression (SVR) and artificial neural networks (ANN) and combination of them with wavelet transforms (WSVR and WANN) were investigated for predicting evaporation rates at Tabriz (Iran) and Antalya (Turkey) stations. For evaluating the performances of studied techniques, four different statistical indicators were utilized namely the root mean square error (RMSE), the mean absolute error (MAE), the correlation coefficient (R), and Nash–Sutcliffe efﬁciency (NSE). Additionally, Taylor diagrams were implemented to test the similarity among the observed and predicted data. Outcomes showed that at Tabriz station, the ANN3 (third input combination that are air temperatures and solar radiation used by ANN) with RMSE of 0.701, MAE of 0.525, R of 0.990 and NSE of 0.977 had better performances in comparison with WANN, SVR and WSVR. So, the wavelet transforms did not have positive effects in increasing the precision of ANN and SVR predictions at Tabriz station. Also, approximately the same trend was seen at Antalya station. In other words, ANN5 (fifth input combination that are air temperatures, relative humidity and solar radiation used by ANN) with RMSE of 0.923, MAE of 0.697, R of 0.962 and NSE of 0.898 had a more accurate predictions among others. Conversely, wavelet transform reduced the prediction errors of SVR at Antalya station. So, the WSVR5 with RMSE of 1.027, MAE of 0.728, R of 0.950 and NSE of 0.870 predicted evaporation rates of Antalya station more precisely than other SVR models. As a conclusion, results from the current study proved that ANN provided reasonable trends for evaporation modeling at both Tabriz and Antalya stations.

Highlights

Evaporation is playing a prominent role in the processes of hydrologic cycle
Because T has the highest correlation with PE in Tabriz and Antalya stations, it has been used in all input combinations to increase prediction accuracy
Seven different scenarios including different effective meteorological parameters have been defined for analyzing the effectiveness of each parameter in increasing the prediction accuracies

Summary

Introduction

Evaporation is playing a prominent role in the processes of hydrologic cycle. Estimation of this phenomenon is necessary as it affects water resources, especially in arid and semi-arid areas like Iran. It is of great significance to estimate the evaporation amount for water resource monitoring. In regions with hot weather and low rainfall, evaporation can lead to losing the noticeable amount of water, it causes decreasing in the water surface elevation. There are two different (direct and indirect) approaches for estimation of evaporation from various variables. Measuring pan is the most prevalent direct method for specifying the value of evaporation (Stanhill, 2002). Calculating evaporation by using pans appears to be having precise answers but using indirect approaches for approximating pan

Objectives

Methods

Results

Conclusion