Evaluating the capability of hybrid data-driven approaches to forecast monthly streamflow using hydrometric and meteorological variables

Streamflow forecasting, as one of the most important issues in hydrological studies, plays a vital role in several aspects of water resources management such as reservoir operation, water allocation, and flood forecasting. In this study, wavelet-gene expression programming (WGEP) and wavelet-M5 prim...

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Autores principales:	Fariba Azarpira, Sajad Shahabi
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	gene expression programming model tree time series wavelet Information technology T58.5-58.64 Environmental technology. Sanitary engineering TD1-1066
Acceso en línea:	https://doaj.org/article/3dd1384a90434c8897318440cb95ac8a
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spelling	oai:doaj.org-article:3dd1384a90434c8897318440cb95ac8a2021-11-23T18:48:32ZEvaluating the capability of hybrid data-driven approaches to forecast monthly streamflow using hydrometric and meteorological variables1464-71411465-173410.2166/hydro.2021.105https://doaj.org/article/3dd1384a90434c8897318440cb95ac8a2021-11-01T00:00:00Zhttp://jh.iwaponline.com/content/23/6/1165https://doaj.org/toc/1464-7141https://doaj.org/toc/1465-1734Streamflow forecasting, as one of the most important issues in hydrological studies, plays a vital role in several aspects of water resources management such as reservoir operation, water allocation, and flood forecasting. In this study, wavelet-gene expression programming (WGEP) and wavelet-M5 prime (WM5P) techniques, as two robust artificial intelligence (AI) models, were applied for forecasting the monthly streamflow in Khoshkroud and Polroud Rivers located in two basins with the same names. Results of hybrid AI techniques were compared with those achieved by two stand-alone models of GEP and M5P. Seven combinations of hydrological (H) and meteorological (M) variables were considered to investigate the effect of climatic variables on the performance of the proposed techniques. Moreover, the performance of both stand-alone and hybrid models were evaluated by statistical criteria of correlation of coefficient, root-mean-square error, index of agreement, the Nash–Sutcliffe model efficiency coefficient, and relative improvement. The statistical results revealed that there is a dependency between ‘the M5P and GEP performance’ and ‘the geometric properties of basins (e.g., area, shape, slope, and river network density)’. It was found that a preprocessed technique could increase the performance of M5P and GEP models. Compared to the stand-alone techniques, the hybrid AI models resulted in higher performance. For both basins, the performance of the WM5P model was higher than the WGEP model, especially for extreme events. Overall, the results demonstrated that the proposed hybrid AI approaches are reliable tools for forecasting the monthly streamflow, while the meteorological and hydrometric variables are taken into account. HIGHLIGHTS Wavelet-based approaches (WGEP and WM5P) are taken to forecast monthly river flow.; Preprocessing of time series improves the capability of forecasting models.; Meteorological parameters increase the model performance, especially for extremes.; The proposed hybrid approaches successfully improve the performance of models.;Fariba AzarpiraSajad ShahabiIWA Publishingarticlegene expression programmingmodel treetime serieswaveletInformation technologyT58.5-58.64Environmental technology. Sanitary engineeringTD1-1066ENJournal of Hydroinformatics, Vol 23, Iss 6, Pp 1165-1181 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	gene expression programming model tree time series wavelet Information technology T58.5-58.64 Environmental technology. Sanitary engineering TD1-1066
spellingShingle	gene expression programming model tree time series wavelet Information technology T58.5-58.64 Environmental technology. Sanitary engineering TD1-1066 Fariba Azarpira Sajad Shahabi Evaluating the capability of hybrid data-driven approaches to forecast monthly streamflow using hydrometric and meteorological variables
description	Streamflow forecasting, as one of the most important issues in hydrological studies, plays a vital role in several aspects of water resources management such as reservoir operation, water allocation, and flood forecasting. In this study, wavelet-gene expression programming (WGEP) and wavelet-M5 prime (WM5P) techniques, as two robust artificial intelligence (AI) models, were applied for forecasting the monthly streamflow in Khoshkroud and Polroud Rivers located in two basins with the same names. Results of hybrid AI techniques were compared with those achieved by two stand-alone models of GEP and M5P. Seven combinations of hydrological (H) and meteorological (M) variables were considered to investigate the effect of climatic variables on the performance of the proposed techniques. Moreover, the performance of both stand-alone and hybrid models were evaluated by statistical criteria of correlation of coefficient, root-mean-square error, index of agreement, the Nash–Sutcliffe model efficiency coefficient, and relative improvement. The statistical results revealed that there is a dependency between ‘the M5P and GEP performance’ and ‘the geometric properties of basins (e.g., area, shape, slope, and river network density)’. It was found that a preprocessed technique could increase the performance of M5P and GEP models. Compared to the stand-alone techniques, the hybrid AI models resulted in higher performance. For both basins, the performance of the WM5P model was higher than the WGEP model, especially for extreme events. Overall, the results demonstrated that the proposed hybrid AI approaches are reliable tools for forecasting the monthly streamflow, while the meteorological and hydrometric variables are taken into account. HIGHLIGHTS Wavelet-based approaches (WGEP and WM5P) are taken to forecast monthly river flow.; Preprocessing of time series improves the capability of forecasting models.; Meteorological parameters increase the model performance, especially for extremes.; The proposed hybrid approaches successfully improve the performance of models.;
format	article
author	Fariba Azarpira Sajad Shahabi
author_facet	Fariba Azarpira Sajad Shahabi
author_sort	Fariba Azarpira
title	Evaluating the capability of hybrid data-driven approaches to forecast monthly streamflow using hydrometric and meteorological variables
title_short	Evaluating the capability of hybrid data-driven approaches to forecast monthly streamflow using hydrometric and meteorological variables
title_full	Evaluating the capability of hybrid data-driven approaches to forecast monthly streamflow using hydrometric and meteorological variables
title_fullStr	Evaluating the capability of hybrid data-driven approaches to forecast monthly streamflow using hydrometric and meteorological variables
title_full_unstemmed	Evaluating the capability of hybrid data-driven approaches to forecast monthly streamflow using hydrometric and meteorological variables
title_sort	evaluating the capability of hybrid data-driven approaches to forecast monthly streamflow using hydrometric and meteorological variables
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/3dd1384a90434c8897318440cb95ac8a
work_keys_str_mv	AT faribaazarpira evaluatingthecapabilityofhybriddatadrivenapproachestoforecastmonthlystreamflowusinghydrometricandmeteorologicalvariables AT sajadshahabi evaluatingthecapabilityofhybriddatadrivenapproachestoforecastmonthlystreamflowusinghydrometricandmeteorologicalvariables
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Evaluating the capability of hybrid data-driven approaches to forecast monthly streamflow using hydrometric and meteorological variables

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