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...

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Autores principales:	Hassan Esmaeili-Gisavandani, Morteza Lotfirad, Masoud Soori Damirchi Sofla, Afshin Ashrafzadeh
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	hybrid model streamflow simulation watershed modeling Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/4e0bc03d31c84ca88ef2027f0b85cec8
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spelling	oai:doaj.org-article:4e0bc03d31c84ca88ef2027f0b85cec82021-11-09T23:59:59ZImproving the performance of rainfall-runoff models using the gene expression programming approach2040-22442408-935410.2166/wcc.2021.064https://doaj.org/article/4e0bc03d31c84ca88ef2027f0b85cec82021-11-01T00:00:00Zhttp://jwcc.iwaponline.com/content/12/7/3308https://doaj.org/toc/2040-2244https://doaj.org/toc/2408-9354In 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.;Hassan Esmaeili-GisavandaniMorteza LotfiradMasoud Soori Damirchi SoflaAfshin AshrafzadehIWA Publishingarticlehybrid modelstreamflow simulationwatershed modelingEnvironmental technology. Sanitary engineeringTD1-1066Environmental sciencesGE1-350ENJournal of Water and Climate Change, Vol 12, Iss 7, Pp 3308-3329 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	hybrid model streamflow simulation watershed modeling Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
spellingShingle	hybrid model streamflow simulation watershed modeling Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Hassan Esmaeili-Gisavandani Morteza Lotfirad Masoud Soori Damirchi Sofla Afshin Ashrafzadeh Improving the performance of rainfall-runoff models using the gene expression programming approach
description	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.;
format	article
author	Hassan Esmaeili-Gisavandani Morteza Lotfirad Masoud Soori Damirchi Sofla Afshin Ashrafzadeh
author_facet	Hassan Esmaeili-Gisavandani Morteza Lotfirad Masoud Soori Damirchi Sofla Afshin Ashrafzadeh
author_sort	Hassan Esmaeili-Gisavandani
title	Improving the performance of rainfall-runoff models using the gene expression programming approach
title_short	Improving the performance of rainfall-runoff models using the gene expression programming approach
title_full	Improving the performance of rainfall-runoff models using the gene expression programming approach
title_fullStr	Improving the performance of rainfall-runoff models using the gene expression programming approach
title_full_unstemmed	Improving the performance of rainfall-runoff models using the gene expression programming approach
title_sort	improving the performance of rainfall-runoff models using the gene expression programming approach
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/4e0bc03d31c84ca88ef2027f0b85cec8
work_keys_str_mv	AT hassanesmaeiligisavandani improvingtheperformanceofrainfallrunoffmodelsusingthegeneexpressionprogrammingapproach AT mortezalotfirad improvingtheperformanceofrainfallrunoffmodelsusingthegeneexpressionprogrammingapproach AT masoudsooridamirchisofla improvingtheperformanceofrainfallrunoffmodelsusingthegeneexpressionprogrammingapproach AT afshinashrafzadeh improvingtheperformanceofrainfallrunoffmodelsusingthegeneexpressionprogrammingapproach
_version_	1718440741832753152

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

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