Estimation of daily reference evapotranspiration by hybrid singular spectrum analysis-based stochastic gradient boosting

Eyyup Ensar Başakın; Ömer Ekmekcioğlu; Paul C Stoy; Mehmet Özger

doi:10.1016/j.mex.2023.102163

Estimation of daily reference evapotranspiration by hybrid singular spectrum analysis-based stochastic gradient boosting

MethodsX. 2023 Mar 28:10:102163. doi: 10.1016/j.mex.2023.102163. eCollection 2023.

Authors

Eyyup Ensar Başakın¹, Ömer Ekmekcioğlu², Paul C Stoy³, Mehmet Özger¹

Affiliations

¹ Hydraulics Division, Civil Engineering Department, Istanbul Technical University, Maslak, 34469, Istanbul, Türkiye.
² Disaster and Emergency Management Department, Disaster Management Institute, Istanbul Technical University, Maslak, 34469, Istanbul, Türkiye.
³ Department of Biological Systems Engineering, University of Wisconsin-Madison, Madison, WI, 53706, USA.

Abstract

In this study, stochastic gradient boosting (SGB), a commonly-adopted soft computing method, was used to estimate reference evapotranspiration (ETo) for the Adiyaman region of southeastern Türkiye. The FAO-56-Penman-Monteith method was used to calculate ETo, which we then estimated using SGB with maximum temperature, minimum temperature, relative humidity, wind speed, and solar radiation obtained from a meteorological station.•The calculated ETo time series values were decomposed into sub-series using Singular Spectrum Analysis (SSA) to enhance prediction accuracy.•Each sub-series was trained with the first 70% of observations and tested with the remaining 30% via SGB. Final prediction values were obtained by collecting all series predictions.•Three lag times were taken into account during the predictions, and both short-term and long-term ETo values were estimated using the proposed framework. The results were tested with respect to root mean square error (RMSE) and Nash-Sutcliffe efficiency (NSE) indicators for ensuring whether the model produced statically acceptable outcomes.

Keywords: Estimation; Reference evapotranspiration; Singular Spectrum Analysis and Gradient Boosting Machine; Singular spectrum analysis; Stochastic gradient boosting.