Improving the performance of rainfall-runoff models using the gene expression programming approach

In this study, five hydrological models, including the soil and water assessment tool (SWAT), identification of unit hydrograph and component flows from rainfall, evapotranspiration, and streamflow (IHACRES), Hydrologiska Byråns Vattenbalansavdelning (HBV), Australian water balance model (AWBM), and...

Full description

Saved in:
Bibliographic Details
Main Authors: Hassan Esmaeili-Gisavandani, Morteza Lotfirad, Masoud Soori Damirchi Sofla, Afshin Ashrafzadeh
Format: article
Language:EN
Published: IWA Publishing 2021
Subjects:
Online Access:https://doaj.org/article/4e0bc03d31c84ca88ef2027f0b85cec8
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this study, five hydrological models, including the soil and water assessment tool (SWAT), identification of unit hydrograph and component flows from rainfall, evapotranspiration, and streamflow (IHACRES), Hydrologiska Byråns Vattenbalansavdelning (HBV), Australian water balance model (AWBM), and Soil Moisture Accounting (SMA), were used to simulate the flow of the Hablehroud River, north-central Iran. All the models were validated based on the root mean square error (RMSE), coefficient of determination (R2), Nash-Sutcliffe model efficiency coefficient (NS), and Kling-Gupta efficiency (KGE). It was found that SWAT, IHACRES, and HBV had satisfactory results in the calibration phase. However, only the SWAT model had good performance in the validation phase and outperformed the other models. It was also observed that peak flows were generally underestimated by the models. The sensitivity analysis results of the model parameters were also evaluated. A hybrid model was developed using gene expression programming (GEP). According to the error measures, the ensemble model had the best performance in both calibration (NS = 0.79) and validation (NS = 0.56). The GEP combination method can combine model outputs from less accurate individual models and produce a superior river flow estimate. HIGHLIGHTS The semi-distributed entirely conceptual model (SWAT) had relatively better performance than the lumped semi-conceptual models (IHACRES, HBV light, AWBM, and SMA).; The GEP was used to construct a hybrid model by ensembling the five calibrated hydrological models to improve the results.; The ensemble models and SWAT yielded high flow and low flow calculations close to the observed data in both the calibration and validation phases.;