A novel fully hybrid simulation-optimization approach for enhancing the calibration and verification performance of the TUW hydrological model

In this study, a novel fully hybrid simulation-optimization approach is proposed for enhancing the prediction performance of the conceptual Technische Universita center dot t Wien (TUW) hydrological model. In the simulation part of the proposed approach, the TUW model is hybridized with a support vector regression (SVR) model where SVR directly uses the TUW model outputs as input and simulates the runoff process. This hybridized simulation model is then integrated to a hybrid optimization approach where heuristic harmony search (HS) and Nelder-Mead Simplex (NMS) optimization approaches are mutually integrated. The objective of the hybrid HS-NMS optimi-zation approach is to calibrate the associated parameter values of both TUW and SVR models by maximizing the Nash-Sutcliffe efficiency (NSE) calculated between simulated and observed runoff values. The applicability of the proposed approach is evaluated on a Murat sub-basin of the Gediz River Basin (GRB) in Turkey for different simulation and optimization model combinations. Identified results indicated that the proposed fully hybrid simulation-optimization approach not only efficiently calibrates the associated solution parameters, but also improves the prediction performance of the TUW model.