Forecasting river flow using nonlinear dynamics
Abstract A Nearest Neighbor Method (NNM) is used to forecast daily river flows that were measured at a single location over a time period spanning about seventy years. A parsimonious three parameter NNM is developed in the context of Nonlinear Dynamics and the dependence between forecast error and l...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Kember, G. [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
1993 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag 1993 |
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| Übergeordnetes Werk: |
Enthalten in: Stochastic hydrology and hydraulics - Springer-Verlag, 1987, 7(1993), 3 vom: Sept., Seite 205-212 |
|---|---|
| Übergeordnetes Werk: |
volume:7 ; year:1993 ; number:3 ; month:09 ; pages:205-212 |
| Links: |
|---|
| DOI / URN: |
10.1007/BF01585599 |
|---|
| Katalog-ID: |
OLC2058726618 |
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| 520 | |a Abstract A Nearest Neighbor Method (NNM) is used to forecast daily river flows that were measured at a single location over a time period spanning about seventy years. A parsimonious three parameter NNM is developed in the context of Nonlinear Dynamics and the dependence between forecast error and length of history used to construct forecasts is investigated. Comparison is made to Auto-Regressive Integrated Moving Average (ARIMA) models. The NNM is found to provide improved forecasts. | ||
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10.1007/BF01585599 doi (DE-627)OLC2058726618 (DE-He213)BF01585599-p DE-627 ger DE-627 rakwb eng 550 VZ Kember, G. verfasserin aut Forecasting river flow using nonlinear dynamics 1993 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 1993 Abstract A Nearest Neighbor Method (NNM) is used to forecast daily river flows that were measured at a single location over a time period spanning about seventy years. A parsimonious three parameter NNM is developed in the context of Nonlinear Dynamics and the dependence between forecast error and length of history used to construct forecasts is investigated. Comparison is made to Auto-Regressive Integrated Moving Average (ARIMA) models. The NNM is found to provide improved forecasts. Waste Water Water Management Water Pollution Stochastic Process Nonlinear Dynamic Flower, A. C. aut Holubeshen, J. aut Enthalten in Stochastic hydrology and hydraulics Springer-Verlag, 1987 7(1993), 3 vom: Sept., Seite 205-212 (DE-627)129226203 (DE-600)56969-0 (DE-576)01445839X 0931-1955 nnns volume:7 year:1993 number:3 month:09 pages:205-212 https://doi.org/10.1007/BF01585599 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GGO GBV_ILN_40 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2018 GBV_ILN_4012 GBV_ILN_4046 GBV_ILN_4103 GBV_ILN_4700 AR 7 1993 3 09 205-212 |
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10.1007/BF01585599 doi (DE-627)OLC2058726618 (DE-He213)BF01585599-p DE-627 ger DE-627 rakwb eng 550 VZ Kember, G. verfasserin aut Forecasting river flow using nonlinear dynamics 1993 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 1993 Abstract A Nearest Neighbor Method (NNM) is used to forecast daily river flows that were measured at a single location over a time period spanning about seventy years. A parsimonious three parameter NNM is developed in the context of Nonlinear Dynamics and the dependence between forecast error and length of history used to construct forecasts is investigated. Comparison is made to Auto-Regressive Integrated Moving Average (ARIMA) models. The NNM is found to provide improved forecasts. Waste Water Water Management Water Pollution Stochastic Process Nonlinear Dynamic Flower, A. C. aut Holubeshen, J. aut Enthalten in Stochastic hydrology and hydraulics Springer-Verlag, 1987 7(1993), 3 vom: Sept., Seite 205-212 (DE-627)129226203 (DE-600)56969-0 (DE-576)01445839X 0931-1955 nnns volume:7 year:1993 number:3 month:09 pages:205-212 https://doi.org/10.1007/BF01585599 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GGO GBV_ILN_40 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2018 GBV_ILN_4012 GBV_ILN_4046 GBV_ILN_4103 GBV_ILN_4700 AR 7 1993 3 09 205-212 |
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10.1007/BF01585599 doi (DE-627)OLC2058726618 (DE-He213)BF01585599-p DE-627 ger DE-627 rakwb eng 550 VZ Kember, G. verfasserin aut Forecasting river flow using nonlinear dynamics 1993 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 1993 Abstract A Nearest Neighbor Method (NNM) is used to forecast daily river flows that were measured at a single location over a time period spanning about seventy years. A parsimonious three parameter NNM is developed in the context of Nonlinear Dynamics and the dependence between forecast error and length of history used to construct forecasts is investigated. Comparison is made to Auto-Regressive Integrated Moving Average (ARIMA) models. The NNM is found to provide improved forecasts. Waste Water Water Management Water Pollution Stochastic Process Nonlinear Dynamic Flower, A. C. aut Holubeshen, J. aut Enthalten in Stochastic hydrology and hydraulics Springer-Verlag, 1987 7(1993), 3 vom: Sept., Seite 205-212 (DE-627)129226203 (DE-600)56969-0 (DE-576)01445839X 0931-1955 nnns volume:7 year:1993 number:3 month:09 pages:205-212 https://doi.org/10.1007/BF01585599 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GGO GBV_ILN_40 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2018 GBV_ILN_4012 GBV_ILN_4046 GBV_ILN_4103 GBV_ILN_4700 AR 7 1993 3 09 205-212 |
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10.1007/BF01585599 doi (DE-627)OLC2058726618 (DE-He213)BF01585599-p DE-627 ger DE-627 rakwb eng 550 VZ Kember, G. verfasserin aut Forecasting river flow using nonlinear dynamics 1993 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag 1993 Abstract A Nearest Neighbor Method (NNM) is used to forecast daily river flows that were measured at a single location over a time period spanning about seventy years. A parsimonious three parameter NNM is developed in the context of Nonlinear Dynamics and the dependence between forecast error and length of history used to construct forecasts is investigated. Comparison is made to Auto-Regressive Integrated Moving Average (ARIMA) models. The NNM is found to provide improved forecasts. Waste Water Water Management Water Pollution Stochastic Process Nonlinear Dynamic Flower, A. C. aut Holubeshen, J. aut Enthalten in Stochastic hydrology and hydraulics Springer-Verlag, 1987 7(1993), 3 vom: Sept., Seite 205-212 (DE-627)129226203 (DE-600)56969-0 (DE-576)01445839X 0931-1955 nnns volume:7 year:1993 number:3 month:09 pages:205-212 https://doi.org/10.1007/BF01585599 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GGO GBV_ILN_40 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2018 GBV_ILN_4012 GBV_ILN_4046 GBV_ILN_4103 GBV_ILN_4700 AR 7 1993 3 09 205-212 |
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Enthalten in Stochastic hydrology and hydraulics 7(1993), 3 vom: Sept., Seite 205-212 volume:7 year:1993 number:3 month:09 pages:205-212 |
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Abstract A Nearest Neighbor Method (NNM) is used to forecast daily river flows that were measured at a single location over a time period spanning about seventy years. A parsimonious three parameter NNM is developed in the context of Nonlinear Dynamics and the dependence between forecast error and length of history used to construct forecasts is investigated. Comparison is made to Auto-Regressive Integrated Moving Average (ARIMA) models. The NNM is found to provide improved forecasts. © Springer-Verlag 1993 |
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Abstract A Nearest Neighbor Method (NNM) is used to forecast daily river flows that were measured at a single location over a time period spanning about seventy years. A parsimonious three parameter NNM is developed in the context of Nonlinear Dynamics and the dependence between forecast error and length of history used to construct forecasts is investigated. Comparison is made to Auto-Regressive Integrated Moving Average (ARIMA) models. The NNM is found to provide improved forecasts. © Springer-Verlag 1993 |
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Abstract A Nearest Neighbor Method (NNM) is used to forecast daily river flows that were measured at a single location over a time period spanning about seventy years. A parsimonious three parameter NNM is developed in the context of Nonlinear Dynamics and the dependence between forecast error and length of history used to construct forecasts is investigated. Comparison is made to Auto-Regressive Integrated Moving Average (ARIMA) models. The NNM is found to provide improved forecasts. © Springer-Verlag 1993 |
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