Daily suspended sediment estimation using neuro-wavelet models

Abstract This paper proposes the application of neuro-wavelet technique for modeling daily suspended sediment–discharge relationship. The neuro-wavelet models are obtained by combining two methods, artificial neural networks (ANN) and discrete wavelet transform. The accuracy of the neuro-wavelet and...
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Autor*in:	Kişi, Özgür [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2009

Schlagwörter:	Suspended sediment load Neural networks Neuro-wavelet Estimation

Übergeordnetes Werk:	Enthalten in: Geologische Rundschau - Berlin : Springer, 1910, 99(2009), 6 vom: 18. Juni, Seite 1471-1482
Übergeordnetes Werk:	volume:99 ; year:2009 ; number:6 ; day:18 ; month:06 ; pages:1471-1482

Links:	Volltext

DOI / URN:	10.1007/s00531-009-0460-2

Katalog-ID:	SPR006717845

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allfields	10.1007/s00531-009-0460-2 doi (DE-627)SPR006717845 (SPR)s00531-009-0460-2-e DE-627 ger DE-627 rakwb eng 550 ASE 38.10 bkl Kişi, Özgür verfasserin aut Daily suspended sediment estimation using neuro-wavelet models 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper proposes the application of neuro-wavelet technique for modeling daily suspended sediment–discharge relationship. The neuro-wavelet models are obtained by combining two methods, artificial neural networks (ANN) and discrete wavelet transform. The accuracy of the neuro-wavelet and the ANN models is compared with each other in suspended sediment load estimation. The daily streamflow and suspended sediment data from two stations on Tongue River in Montana are used as case studies. The comparison results reveal that the suggested model could increase the estimation accuracy. Suspended sediment load (dpeaa)DE-He213 Neural networks (dpeaa)DE-He213 Neuro-wavelet (dpeaa)DE-He213 Estimation (dpeaa)DE-He213 Enthalten in Geologische Rundschau Berlin : Springer, 1910 99(2009), 6 vom: 18. Juni, Seite 1471-1482 (DE-627)47265019X (DE-600)2168407-8 1432-1149 nnns volume:99 year:2009 number:6 day:18 month:06 pages:1471-1482 https://dx.doi.org/10.1007/s00531-009-0460-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 38.10 ASE AR 99 2009 6 18 06 1471-1482
spelling	10.1007/s00531-009-0460-2 doi (DE-627)SPR006717845 (SPR)s00531-009-0460-2-e DE-627 ger DE-627 rakwb eng 550 ASE 38.10 bkl Kişi, Özgür verfasserin aut Daily suspended sediment estimation using neuro-wavelet models 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper proposes the application of neuro-wavelet technique for modeling daily suspended sediment–discharge relationship. The neuro-wavelet models are obtained by combining two methods, artificial neural networks (ANN) and discrete wavelet transform. The accuracy of the neuro-wavelet and the ANN models is compared with each other in suspended sediment load estimation. The daily streamflow and suspended sediment data from two stations on Tongue River in Montana are used as case studies. The comparison results reveal that the suggested model could increase the estimation accuracy. Suspended sediment load (dpeaa)DE-He213 Neural networks (dpeaa)DE-He213 Neuro-wavelet (dpeaa)DE-He213 Estimation (dpeaa)DE-He213 Enthalten in Geologische Rundschau Berlin : Springer, 1910 99(2009), 6 vom: 18. Juni, Seite 1471-1482 (DE-627)47265019X (DE-600)2168407-8 1432-1149 nnns volume:99 year:2009 number:6 day:18 month:06 pages:1471-1482 https://dx.doi.org/10.1007/s00531-009-0460-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 38.10 ASE AR 99 2009 6 18 06 1471-1482
allfields_unstemmed	10.1007/s00531-009-0460-2 doi (DE-627)SPR006717845 (SPR)s00531-009-0460-2-e DE-627 ger DE-627 rakwb eng 550 ASE 38.10 bkl Kişi, Özgür verfasserin aut Daily suspended sediment estimation using neuro-wavelet models 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper proposes the application of neuro-wavelet technique for modeling daily suspended sediment–discharge relationship. The neuro-wavelet models are obtained by combining two methods, artificial neural networks (ANN) and discrete wavelet transform. The accuracy of the neuro-wavelet and the ANN models is compared with each other in suspended sediment load estimation. The daily streamflow and suspended sediment data from two stations on Tongue River in Montana are used as case studies. The comparison results reveal that the suggested model could increase the estimation accuracy. Suspended sediment load (dpeaa)DE-He213 Neural networks (dpeaa)DE-He213 Neuro-wavelet (dpeaa)DE-He213 Estimation (dpeaa)DE-He213 Enthalten in Geologische Rundschau Berlin : Springer, 1910 99(2009), 6 vom: 18. Juni, Seite 1471-1482 (DE-627)47265019X (DE-600)2168407-8 1432-1149 nnns volume:99 year:2009 number:6 day:18 month:06 pages:1471-1482 https://dx.doi.org/10.1007/s00531-009-0460-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 38.10 ASE AR 99 2009 6 18 06 1471-1482
allfieldsGer	10.1007/s00531-009-0460-2 doi (DE-627)SPR006717845 (SPR)s00531-009-0460-2-e DE-627 ger DE-627 rakwb eng 550 ASE 38.10 bkl Kişi, Özgür verfasserin aut Daily suspended sediment estimation using neuro-wavelet models 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper proposes the application of neuro-wavelet technique for modeling daily suspended sediment–discharge relationship. The neuro-wavelet models are obtained by combining two methods, artificial neural networks (ANN) and discrete wavelet transform. The accuracy of the neuro-wavelet and the ANN models is compared with each other in suspended sediment load estimation. The daily streamflow and suspended sediment data from two stations on Tongue River in Montana are used as case studies. The comparison results reveal that the suggested model could increase the estimation accuracy. Suspended sediment load (dpeaa)DE-He213 Neural networks (dpeaa)DE-He213 Neuro-wavelet (dpeaa)DE-He213 Estimation (dpeaa)DE-He213 Enthalten in Geologische Rundschau Berlin : Springer, 1910 99(2009), 6 vom: 18. Juni, Seite 1471-1482 (DE-627)47265019X (DE-600)2168407-8 1432-1149 nnns volume:99 year:2009 number:6 day:18 month:06 pages:1471-1482 https://dx.doi.org/10.1007/s00531-009-0460-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 38.10 ASE AR 99 2009 6 18 06 1471-1482
allfieldsSound	10.1007/s00531-009-0460-2 doi (DE-627)SPR006717845 (SPR)s00531-009-0460-2-e DE-627 ger DE-627 rakwb eng 550 ASE 38.10 bkl Kişi, Özgür verfasserin aut Daily suspended sediment estimation using neuro-wavelet models 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract This paper proposes the application of neuro-wavelet technique for modeling daily suspended sediment–discharge relationship. The neuro-wavelet models are obtained by combining two methods, artificial neural networks (ANN) and discrete wavelet transform. The accuracy of the neuro-wavelet and the ANN models is compared with each other in suspended sediment load estimation. The daily streamflow and suspended sediment data from two stations on Tongue River in Montana are used as case studies. The comparison results reveal that the suggested model could increase the estimation accuracy. Suspended sediment load (dpeaa)DE-He213 Neural networks (dpeaa)DE-He213 Neuro-wavelet (dpeaa)DE-He213 Estimation (dpeaa)DE-He213 Enthalten in Geologische Rundschau Berlin : Springer, 1910 99(2009), 6 vom: 18. Juni, Seite 1471-1482 (DE-627)47265019X (DE-600)2168407-8 1432-1149 nnns volume:99 year:2009 number:6 day:18 month:06 pages:1471-1482 https://dx.doi.org/10.1007/s00531-009-0460-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OPC-GGO SSG-OPC-ASE GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_647 GBV_ILN_702 38.10 ASE AR 99 2009 6 18 06 1471-1482
language	English
source	Enthalten in Geologische Rundschau 99(2009), 6 vom: 18. Juni, Seite 1471-1482 volume:99 year:2009 number:6 day:18 month:06 pages:1471-1482
sourceStr	Enthalten in Geologische Rundschau 99(2009), 6 vom: 18. Juni, Seite 1471-1482 volume:99 year:2009 number:6 day:18 month:06 pages:1471-1482
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topic_facet	Suspended sediment load Neural networks Neuro-wavelet Estimation
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author	Kişi, Özgür
spellingShingle	Kişi, Özgür ddc 550 bkl 38.10 misc Suspended sediment load misc Neural networks misc Neuro-wavelet misc Estimation Daily suspended sediment estimation using neuro-wavelet models
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topic_title	550 ASE 38.10 bkl Daily suspended sediment estimation using neuro-wavelet models Suspended sediment load (dpeaa)DE-He213 Neural networks (dpeaa)DE-He213 Neuro-wavelet (dpeaa)DE-He213 Estimation (dpeaa)DE-He213
topic	ddc 550 bkl 38.10 misc Suspended sediment load misc Neural networks misc Neuro-wavelet misc Estimation
topic_unstemmed	ddc 550 bkl 38.10 misc Suspended sediment load misc Neural networks misc Neuro-wavelet misc Estimation
topic_browse	ddc 550 bkl 38.10 misc Suspended sediment load misc Neural networks misc Neuro-wavelet misc Estimation
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title	Daily suspended sediment estimation using neuro-wavelet models
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title_full	Daily suspended sediment estimation using neuro-wavelet models
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title_sort	daily suspended sediment estimation using neuro-wavelet models
title_auth	Daily suspended sediment estimation using neuro-wavelet models
abstract	Abstract This paper proposes the application of neuro-wavelet technique for modeling daily suspended sediment–discharge relationship. The neuro-wavelet models are obtained by combining two methods, artificial neural networks (ANN) and discrete wavelet transform. The accuracy of the neuro-wavelet and the ANN models is compared with each other in suspended sediment load estimation. The daily streamflow and suspended sediment data from two stations on Tongue River in Montana are used as case studies. The comparison results reveal that the suggested model could increase the estimation accuracy.
abstractGer	Abstract This paper proposes the application of neuro-wavelet technique for modeling daily suspended sediment–discharge relationship. The neuro-wavelet models are obtained by combining two methods, artificial neural networks (ANN) and discrete wavelet transform. The accuracy of the neuro-wavelet and the ANN models is compared with each other in suspended sediment load estimation. The daily streamflow and suspended sediment data from two stations on Tongue River in Montana are used as case studies. The comparison results reveal that the suggested model could increase the estimation accuracy.
abstract_unstemmed	Abstract This paper proposes the application of neuro-wavelet technique for modeling daily suspended sediment–discharge relationship. The neuro-wavelet models are obtained by combining two methods, artificial neural networks (ANN) and discrete wavelet transform. The accuracy of the neuro-wavelet and the ANN models is compared with each other in suspended sediment load estimation. The daily streamflow and suspended sediment data from two stations on Tongue River in Montana are used as case studies. The comparison results reveal that the suggested model could increase the estimation accuracy.
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container_issue	6
title_short	Daily suspended sediment estimation using neuro-wavelet models
url	https://dx.doi.org/10.1007/s00531-009-0460-2
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up_date	2024-07-04T00:20:16.939Z
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