EEMD- and VMD-based hybrid GPR models for river streamflow point and interval predictions

Reliable river streamflow (RSF) forecasting is an important issue due to its impact on planning and operation of the water resources system. In this study, based on Lower Upper Bound Estimation (LUBE), hybrid artificial intelligence methods were used for point and interval prediction of monthly RSF....

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Autores principales:	Roghayeh Ghasempour, Hazi Mohammad Azamathulla, Kiyoumars Roushangar
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	interval prediction lower upper bound estimation streamflow successive points variational mode decomposition Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506
Acceso en línea:	https://doaj.org/article/9211d0b4060c49ad9b33af48a1389458
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spelling	oai:doaj.org-article:9211d0b4060c49ad9b33af48a13894582021-11-23T18:57:11ZEEMD- and VMD-based hybrid GPR models for river streamflow point and interval predictions1606-97491607-079810.2166/ws.2021.151https://doaj.org/article/9211d0b4060c49ad9b33af48a13894582021-11-01T00:00:00Zhttp://ws.iwaponline.com/content/21/7/3960https://doaj.org/toc/1606-9749https://doaj.org/toc/1607-0798Reliable river streamflow (RSF) forecasting is an important issue due to its impact on planning and operation of the water resources system. In this study, based on Lower Upper Bound Estimation (LUBE), hybrid artificial intelligence methods were used for point and interval prediction of monthly RSF. Two states based on stations' own data and upstream stations' data were considered for RSF modeling of the Housatonic River during the period of 1941–2018. Ensemble Empirical Mode Decomposition (EEMD) and Variational Mode Decomposition (VMD) methods were used for enhancing the streamflow point forecasting accuracy. Interval Prediction (PI) was applied for tolerating increased uncertainty. Results showed that in state 1, the error criterion value for the superior model decreased from 0.155 to 0.082 and 0.09 for the EEMD- and VMD-based models, respectively. Generally, hybrid models increased the modeling accuracy between 20% and 40%. Via the integrated approaches, the upstream stations' data was successfully used for streamflow prediction of stations without data. In this state, the PI coverage probability values for the VMD-based model were approximately 12% higher than the single model. Generally, the VMD-based model led to more desirable results due to having higher PI coverage probability and lower mean PI width values. HIGHLIGHTS AI methods were applied to model the RSF in successive hydrometric stations.; To obtain a model with higher efficiency, EEMD and VMD techniques were applied.; Interval prediction was applied for providing more details for practical operation decisions.;Roghayeh GhasempourHazi Mohammad AzamathullaKiyoumars RoushangarIWA Publishingarticleinterval predictionlower upper bound estimationstreamflowsuccessive pointsvariational mode decompositionWater supply for domestic and industrial purposesTD201-500River, lake, and water-supply engineering (General)TC401-506ENWater Supply, Vol 21, Iss 7, Pp 3960-3975 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	interval prediction lower upper bound estimation streamflow successive points variational mode decomposition Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506
spellingShingle	interval prediction lower upper bound estimation streamflow successive points variational mode decomposition Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506 Roghayeh Ghasempour Hazi Mohammad Azamathulla Kiyoumars Roushangar EEMD- and VMD-based hybrid GPR models for river streamflow point and interval predictions
description	Reliable river streamflow (RSF) forecasting is an important issue due to its impact on planning and operation of the water resources system. In this study, based on Lower Upper Bound Estimation (LUBE), hybrid artificial intelligence methods were used for point and interval prediction of monthly RSF. Two states based on stations' own data and upstream stations' data were considered for RSF modeling of the Housatonic River during the period of 1941–2018. Ensemble Empirical Mode Decomposition (EEMD) and Variational Mode Decomposition (VMD) methods were used for enhancing the streamflow point forecasting accuracy. Interval Prediction (PI) was applied for tolerating increased uncertainty. Results showed that in state 1, the error criterion value for the superior model decreased from 0.155 to 0.082 and 0.09 for the EEMD- and VMD-based models, respectively. Generally, hybrid models increased the modeling accuracy between 20% and 40%. Via the integrated approaches, the upstream stations' data was successfully used for streamflow prediction of stations without data. In this state, the PI coverage probability values for the VMD-based model were approximately 12% higher than the single model. Generally, the VMD-based model led to more desirable results due to having higher PI coverage probability and lower mean PI width values. HIGHLIGHTS AI methods were applied to model the RSF in successive hydrometric stations.; To obtain a model with higher efficiency, EEMD and VMD techniques were applied.; Interval prediction was applied for providing more details for practical operation decisions.;
format	article
author	Roghayeh Ghasempour Hazi Mohammad Azamathulla Kiyoumars Roushangar
author_facet	Roghayeh Ghasempour Hazi Mohammad Azamathulla Kiyoumars Roushangar
author_sort	Roghayeh Ghasempour
title	EEMD- and VMD-based hybrid GPR models for river streamflow point and interval predictions
title_short	EEMD- and VMD-based hybrid GPR models for river streamflow point and interval predictions
title_full	EEMD- and VMD-based hybrid GPR models for river streamflow point and interval predictions
title_fullStr	EEMD- and VMD-based hybrid GPR models for river streamflow point and interval predictions
title_full_unstemmed	EEMD- and VMD-based hybrid GPR models for river streamflow point and interval predictions
title_sort	eemd- and vmd-based hybrid gpr models for river streamflow point and interval predictions
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/9211d0b4060c49ad9b33af48a1389458
work_keys_str_mv	AT roghayehghasempour eemdandvmdbasedhybridgprmodelsforriverstreamflowpointandintervalpredictions AT hazimohammadazamathulla eemdandvmdbasedhybridgprmodelsforriverstreamflowpointandintervalpredictions AT kiyoumarsroushangar eemdandvmdbasedhybridgprmodelsforriverstreamflowpointandintervalpredictions
_version_	1718416166416809984

EEMD- and VMD-based hybrid GPR models for river streamflow point and interval predictions

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