Uncertainty analysis of monthly river flow modeling in consecutive hydrometric stations using integrated data-driven models

The ﬂow assessment in a river is of vital interest in hydraulic engineering for ﬂood warning and evacuation measures. To operate water structures more efﬁciently, models that forecast river discharge are desired to be of high precision and certain degree of accuracy. Therefore, in this study, two ar...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Karim Amininia, Seyed Mahdi Saghebian
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	consecutive stations emd kelm pre-processing river discharge Information technology T58.5-58.64 Environmental technology. Sanitary engineering TD1-1066
Acceso en línea:	https://doaj.org/article/b8ba6f288e6d4f13a2fc3af6a3100008
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:b8ba6f288e6d4f13a2fc3af6a3100008
record_format	dspace
spelling	oai:doaj.org-article:b8ba6f288e6d4f13a2fc3af6a31000082021-11-05T17:49:10ZUncertainty analysis of monthly river flow modeling in consecutive hydrometric stations using integrated data-driven models1464-71411465-173410.2166/hydro.2021.142https://doaj.org/article/b8ba6f288e6d4f13a2fc3af6a31000082021-07-01T00:00:00Zhttp://jh.iwaponline.com/content/23/4/897https://doaj.org/toc/1464-7141https://doaj.org/toc/1465-1734The ﬂow assessment in a river is of vital interest in hydraulic engineering for ﬂood warning and evacuation measures. To operate water structures more efﬁciently, models that forecast river discharge are desired to be of high precision and certain degree of accuracy. Therefore, in this study, two artificial intelligence models, namely kernel extreme learning machine (KELM) and multivariate adaptive regression splines (MARS), were applied for the monthly river flow (MRF) modeling. For this aim, Mississippi river with three consecutive hydrometric stations was selected as case study. Using the previous MRF values during the period of 1950–2019, several models were developed and tested under two scenarios (i.e. modeling based on station's own data or previous station's data). Wavelet transform (WT) and ensemble empirical mode decomposition (EEMD) as data processing approaches were used for enhancing modeling capability. Obtained results indicated that the integrated models resulted in more accurate outcomes. Data processing enhanced the model's capability up to 25%. It was observed that the previous station's data could be applied successfully for MRF modeling when the station's own data were not available. The best-applied model dependability was assessed via uncertainty analysis, and an allowable degree of uncertainty was found in MRF modeling. HIGHLIGHTS Kernel extreme learning machine (KELM) and multivariate adaptive regression splines (MARS) approaches were used for MRF modeling in three successive hydrometric stations.; The WT and EEMD as pre-processing methods were used for improving the model's efficiency.; Monte Carlo uncertainty analysis was applied to investigate the dependability of the applied models.;Karim AmininiaSeyed Mahdi SaghebianIWA Publishingarticleconsecutive stationsemdkelmpre-processingriver dischargeInformation technologyT58.5-58.64Environmental technology. Sanitary engineeringTD1-1066ENJournal of Hydroinformatics, Vol 23, Iss 4, Pp 897-913 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	consecutive stations emd kelm pre-processing river discharge Information technology T58.5-58.64 Environmental technology. Sanitary engineering TD1-1066
spellingShingle	consecutive stations emd kelm pre-processing river discharge Information technology T58.5-58.64 Environmental technology. Sanitary engineering TD1-1066 Karim Amininia Seyed Mahdi Saghebian Uncertainty analysis of monthly river flow modeling in consecutive hydrometric stations using integrated data-driven models
description	The ﬂow assessment in a river is of vital interest in hydraulic engineering for ﬂood warning and evacuation measures. To operate water structures more efﬁciently, models that forecast river discharge are desired to be of high precision and certain degree of accuracy. Therefore, in this study, two artificial intelligence models, namely kernel extreme learning machine (KELM) and multivariate adaptive regression splines (MARS), were applied for the monthly river flow (MRF) modeling. For this aim, Mississippi river with three consecutive hydrometric stations was selected as case study. Using the previous MRF values during the period of 1950–2019, several models were developed and tested under two scenarios (i.e. modeling based on station's own data or previous station's data). Wavelet transform (WT) and ensemble empirical mode decomposition (EEMD) as data processing approaches were used for enhancing modeling capability. Obtained results indicated that the integrated models resulted in more accurate outcomes. Data processing enhanced the model's capability up to 25%. It was observed that the previous station's data could be applied successfully for MRF modeling when the station's own data were not available. The best-applied model dependability was assessed via uncertainty analysis, and an allowable degree of uncertainty was found in MRF modeling. HIGHLIGHTS Kernel extreme learning machine (KELM) and multivariate adaptive regression splines (MARS) approaches were used for MRF modeling in three successive hydrometric stations.; The WT and EEMD as pre-processing methods were used for improving the model's efficiency.; Monte Carlo uncertainty analysis was applied to investigate the dependability of the applied models.;
format	article
author	Karim Amininia Seyed Mahdi Saghebian
author_facet	Karim Amininia Seyed Mahdi Saghebian
author_sort	Karim Amininia
title	Uncertainty analysis of monthly river flow modeling in consecutive hydrometric stations using integrated data-driven models
title_short	Uncertainty analysis of monthly river flow modeling in consecutive hydrometric stations using integrated data-driven models
title_full	Uncertainty analysis of monthly river flow modeling in consecutive hydrometric stations using integrated data-driven models
title_fullStr	Uncertainty analysis of monthly river flow modeling in consecutive hydrometric stations using integrated data-driven models
title_full_unstemmed	Uncertainty analysis of monthly river flow modeling in consecutive hydrometric stations using integrated data-driven models
title_sort	uncertainty analysis of monthly river flow modeling in consecutive hydrometric stations using integrated data-driven models
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/b8ba6f288e6d4f13a2fc3af6a3100008
work_keys_str_mv	AT karimamininia uncertaintyanalysisofmonthlyriverflowmodelinginconsecutivehydrometricstationsusingintegrateddatadrivenmodels AT seyedmahdisaghebian uncertaintyanalysisofmonthlyriverflowmodelinginconsecutivehydrometricstationsusingintegrateddatadrivenmodels
_version_	1718444121286246400

Uncertainty analysis of monthly river flow modeling in consecutive hydrometric stations using integrated data-driven models

Ejemplares similares