Communicating physics-based wave model predictions of coral reefs using Bayesian belief networks
The use of physics-based wave propagation predictions requires a considerable time commitment, a high level of expertise and extensive climate and reef data that are not always available when undertaking planning for management of coasts and coral reef ecosystems. Bayesian belief networks (BBNs) hav...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Callaghan, David P. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018transfer abstract |
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| Schlagwörter: |
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| Umfang: |
10 |
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| Übergeordnetes Werk: |
Enthalten in: Long term evolution of Molniya orbit under the effect of Earth’s non-spherical gravitational perturbation - Zhu, Ting-Lei ELSEVIER, 2014, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:108 ; year:2018 ; pages:123-132 ; extent:10 |
| Links: |
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| DOI / URN: |
10.1016/j.envsoft.2018.07.021 |
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| Katalog-ID: |
ELV043868827 |
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| 520 | |a The use of physics-based wave propagation predictions requires a considerable time commitment, a high level of expertise and extensive climate and reef data that are not always available when undertaking planning for management of coasts and coral reef ecosystems. Bayesian belief networks (BBNs) have at least three attributes that make them an excellent choice to communicate physics-based wave model predictions. First, BBNs subsume thousands of predictions to provide probabilistic outcomes. Second, by using prior probabilities, a practitioner can still obtain predictions of wave outcomes even when their knowledge of input parameters is incomplete. Third, BBNs can propagate evidence from outputs to inputs, which can be used to identify input conditions that are most likely to deliver a chosen outcome. These three attributes are tested and found to hold for a BBN developed for this purpose. | ||
| 520 | |a The use of physics-based wave propagation predictions requires a considerable time commitment, a high level of expertise and extensive climate and reef data that are not always available when undertaking planning for management of coasts and coral reef ecosystems. Bayesian belief networks (BBNs) have at least three attributes that make them an excellent choice to communicate physics-based wave model predictions. First, BBNs subsume thousands of predictions to provide probabilistic outcomes. Second, by using prior probabilities, a practitioner can still obtain predictions of wave outcomes even when their knowledge of input parameters is incomplete. Third, BBNs can propagate evidence from outputs to inputs, which can be used to identify input conditions that are most likely to deliver a chosen outcome. These three attributes are tested and found to hold for a BBN developed for this purpose. | ||
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10.1016/j.envsoft.2018.07.021 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000998.pica (DE-627)ELV043868827 (ELSEVIER)S1364-8152(17)31177-5 DE-627 ger DE-627 rakwb eng 520 VZ 620 VZ 610 570 VZ 44.89 bkl Callaghan, David P. verfasserin aut Communicating physics-based wave model predictions of coral reefs using Bayesian belief networks 2018transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The use of physics-based wave propagation predictions requires a considerable time commitment, a high level of expertise and extensive climate and reef data that are not always available when undertaking planning for management of coasts and coral reef ecosystems. Bayesian belief networks (BBNs) have at least three attributes that make them an excellent choice to communicate physics-based wave model predictions. First, BBNs subsume thousands of predictions to provide probabilistic outcomes. Second, by using prior probabilities, a practitioner can still obtain predictions of wave outcomes even when their knowledge of input parameters is incomplete. Third, BBNs can propagate evidence from outputs to inputs, which can be used to identify input conditions that are most likely to deliver a chosen outcome. These three attributes are tested and found to hold for a BBN developed for this purpose. The use of physics-based wave propagation predictions requires a considerable time commitment, a high level of expertise and extensive climate and reef data that are not always available when undertaking planning for management of coasts and coral reef ecosystems. Bayesian belief networks (BBNs) have at least three attributes that make them an excellent choice to communicate physics-based wave model predictions. First, BBNs subsume thousands of predictions to provide probabilistic outcomes. Second, by using prior probabilities, a practitioner can still obtain predictions of wave outcomes even when their knowledge of input parameters is incomplete. Third, BBNs can propagate evidence from outputs to inputs, which can be used to identify input conditions that are most likely to deliver a chosen outcome. These three attributes are tested and found to hold for a BBN developed for this purpose. Physics-based wave modelling Elsevier SWAN Elsevier End user Elsevier Bayesian belief networks Elsevier Communication Elsevier Baldock, Tom E. oth Shabani, Behnam oth Mumby, Peter J. oth Enthalten in Elsevier Science Zhu, Ting-Lei ELSEVIER Long term evolution of Molniya orbit under the effect of Earth’s non-spherical gravitational perturbation 2014 Amsterdam [u.a.] (DE-627)ELV017414318 volume:108 year:2018 pages:123-132 extent:10 https://doi.org/10.1016/j.envsoft.2018.07.021 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_70 44.89 Endokrinologie VZ AR 108 2018 123-132 10 |
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10.1016/j.envsoft.2018.07.021 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000998.pica (DE-627)ELV043868827 (ELSEVIER)S1364-8152(17)31177-5 DE-627 ger DE-627 rakwb eng 520 VZ 620 VZ 610 570 VZ 44.89 bkl Callaghan, David P. verfasserin aut Communicating physics-based wave model predictions of coral reefs using Bayesian belief networks 2018transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The use of physics-based wave propagation predictions requires a considerable time commitment, a high level of expertise and extensive climate and reef data that are not always available when undertaking planning for management of coasts and coral reef ecosystems. Bayesian belief networks (BBNs) have at least three attributes that make them an excellent choice to communicate physics-based wave model predictions. First, BBNs subsume thousands of predictions to provide probabilistic outcomes. Second, by using prior probabilities, a practitioner can still obtain predictions of wave outcomes even when their knowledge of input parameters is incomplete. Third, BBNs can propagate evidence from outputs to inputs, which can be used to identify input conditions that are most likely to deliver a chosen outcome. These three attributes are tested and found to hold for a BBN developed for this purpose. The use of physics-based wave propagation predictions requires a considerable time commitment, a high level of expertise and extensive climate and reef data that are not always available when undertaking planning for management of coasts and coral reef ecosystems. Bayesian belief networks (BBNs) have at least three attributes that make them an excellent choice to communicate physics-based wave model predictions. First, BBNs subsume thousands of predictions to provide probabilistic outcomes. Second, by using prior probabilities, a practitioner can still obtain predictions of wave outcomes even when their knowledge of input parameters is incomplete. Third, BBNs can propagate evidence from outputs to inputs, which can be used to identify input conditions that are most likely to deliver a chosen outcome. These three attributes are tested and found to hold for a BBN developed for this purpose. Physics-based wave modelling Elsevier SWAN Elsevier End user Elsevier Bayesian belief networks Elsevier Communication Elsevier Baldock, Tom E. oth Shabani, Behnam oth Mumby, Peter J. oth Enthalten in Elsevier Science Zhu, Ting-Lei ELSEVIER Long term evolution of Molniya orbit under the effect of Earth’s non-spherical gravitational perturbation 2014 Amsterdam [u.a.] (DE-627)ELV017414318 volume:108 year:2018 pages:123-132 extent:10 https://doi.org/10.1016/j.envsoft.2018.07.021 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_70 44.89 Endokrinologie VZ AR 108 2018 123-132 10 |
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10.1016/j.envsoft.2018.07.021 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000998.pica (DE-627)ELV043868827 (ELSEVIER)S1364-8152(17)31177-5 DE-627 ger DE-627 rakwb eng 520 VZ 620 VZ 610 570 VZ 44.89 bkl Callaghan, David P. verfasserin aut Communicating physics-based wave model predictions of coral reefs using Bayesian belief networks 2018transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The use of physics-based wave propagation predictions requires a considerable time commitment, a high level of expertise and extensive climate and reef data that are not always available when undertaking planning for management of coasts and coral reef ecosystems. Bayesian belief networks (BBNs) have at least three attributes that make them an excellent choice to communicate physics-based wave model predictions. First, BBNs subsume thousands of predictions to provide probabilistic outcomes. Second, by using prior probabilities, a practitioner can still obtain predictions of wave outcomes even when their knowledge of input parameters is incomplete. Third, BBNs can propagate evidence from outputs to inputs, which can be used to identify input conditions that are most likely to deliver a chosen outcome. These three attributes are tested and found to hold for a BBN developed for this purpose. The use of physics-based wave propagation predictions requires a considerable time commitment, a high level of expertise and extensive climate and reef data that are not always available when undertaking planning for management of coasts and coral reef ecosystems. Bayesian belief networks (BBNs) have at least three attributes that make them an excellent choice to communicate physics-based wave model predictions. First, BBNs subsume thousands of predictions to provide probabilistic outcomes. Second, by using prior probabilities, a practitioner can still obtain predictions of wave outcomes even when their knowledge of input parameters is incomplete. Third, BBNs can propagate evidence from outputs to inputs, which can be used to identify input conditions that are most likely to deliver a chosen outcome. These three attributes are tested and found to hold for a BBN developed for this purpose. Physics-based wave modelling Elsevier SWAN Elsevier End user Elsevier Bayesian belief networks Elsevier Communication Elsevier Baldock, Tom E. oth Shabani, Behnam oth Mumby, Peter J. oth Enthalten in Elsevier Science Zhu, Ting-Lei ELSEVIER Long term evolution of Molniya orbit under the effect of Earth’s non-spherical gravitational perturbation 2014 Amsterdam [u.a.] (DE-627)ELV017414318 volume:108 year:2018 pages:123-132 extent:10 https://doi.org/10.1016/j.envsoft.2018.07.021 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_70 44.89 Endokrinologie VZ AR 108 2018 123-132 10 |
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10.1016/j.envsoft.2018.07.021 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000998.pica (DE-627)ELV043868827 (ELSEVIER)S1364-8152(17)31177-5 DE-627 ger DE-627 rakwb eng 520 VZ 620 VZ 610 570 VZ 44.89 bkl Callaghan, David P. verfasserin aut Communicating physics-based wave model predictions of coral reefs using Bayesian belief networks 2018transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The use of physics-based wave propagation predictions requires a considerable time commitment, a high level of expertise and extensive climate and reef data that are not always available when undertaking planning for management of coasts and coral reef ecosystems. Bayesian belief networks (BBNs) have at least three attributes that make them an excellent choice to communicate physics-based wave model predictions. First, BBNs subsume thousands of predictions to provide probabilistic outcomes. Second, by using prior probabilities, a practitioner can still obtain predictions of wave outcomes even when their knowledge of input parameters is incomplete. Third, BBNs can propagate evidence from outputs to inputs, which can be used to identify input conditions that are most likely to deliver a chosen outcome. These three attributes are tested and found to hold for a BBN developed for this purpose. The use of physics-based wave propagation predictions requires a considerable time commitment, a high level of expertise and extensive climate and reef data that are not always available when undertaking planning for management of coasts and coral reef ecosystems. Bayesian belief networks (BBNs) have at least three attributes that make them an excellent choice to communicate physics-based wave model predictions. First, BBNs subsume thousands of predictions to provide probabilistic outcomes. Second, by using prior probabilities, a practitioner can still obtain predictions of wave outcomes even when their knowledge of input parameters is incomplete. Third, BBNs can propagate evidence from outputs to inputs, which can be used to identify input conditions that are most likely to deliver a chosen outcome. These three attributes are tested and found to hold for a BBN developed for this purpose. Physics-based wave modelling Elsevier SWAN Elsevier End user Elsevier Bayesian belief networks Elsevier Communication Elsevier Baldock, Tom E. oth Shabani, Behnam oth Mumby, Peter J. oth Enthalten in Elsevier Science Zhu, Ting-Lei ELSEVIER Long term evolution of Molniya orbit under the effect of Earth’s non-spherical gravitational perturbation 2014 Amsterdam [u.a.] (DE-627)ELV017414318 volume:108 year:2018 pages:123-132 extent:10 https://doi.org/10.1016/j.envsoft.2018.07.021 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_70 44.89 Endokrinologie VZ AR 108 2018 123-132 10 |
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10.1016/j.envsoft.2018.07.021 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000000998.pica (DE-627)ELV043868827 (ELSEVIER)S1364-8152(17)31177-5 DE-627 ger DE-627 rakwb eng 520 VZ 620 VZ 610 570 VZ 44.89 bkl Callaghan, David P. verfasserin aut Communicating physics-based wave model predictions of coral reefs using Bayesian belief networks 2018transfer abstract 10 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The use of physics-based wave propagation predictions requires a considerable time commitment, a high level of expertise and extensive climate and reef data that are not always available when undertaking planning for management of coasts and coral reef ecosystems. Bayesian belief networks (BBNs) have at least three attributes that make them an excellent choice to communicate physics-based wave model predictions. First, BBNs subsume thousands of predictions to provide probabilistic outcomes. Second, by using prior probabilities, a practitioner can still obtain predictions of wave outcomes even when their knowledge of input parameters is incomplete. Third, BBNs can propagate evidence from outputs to inputs, which can be used to identify input conditions that are most likely to deliver a chosen outcome. These three attributes are tested and found to hold for a BBN developed for this purpose. The use of physics-based wave propagation predictions requires a considerable time commitment, a high level of expertise and extensive climate and reef data that are not always available when undertaking planning for management of coasts and coral reef ecosystems. Bayesian belief networks (BBNs) have at least three attributes that make them an excellent choice to communicate physics-based wave model predictions. First, BBNs subsume thousands of predictions to provide probabilistic outcomes. Second, by using prior probabilities, a practitioner can still obtain predictions of wave outcomes even when their knowledge of input parameters is incomplete. Third, BBNs can propagate evidence from outputs to inputs, which can be used to identify input conditions that are most likely to deliver a chosen outcome. These three attributes are tested and found to hold for a BBN developed for this purpose. Physics-based wave modelling Elsevier SWAN Elsevier End user Elsevier Bayesian belief networks Elsevier Communication Elsevier Baldock, Tom E. oth Shabani, Behnam oth Mumby, Peter J. oth Enthalten in Elsevier Science Zhu, Ting-Lei ELSEVIER Long term evolution of Molniya orbit under the effect of Earth’s non-spherical gravitational perturbation 2014 Amsterdam [u.a.] (DE-627)ELV017414318 volume:108 year:2018 pages:123-132 extent:10 https://doi.org/10.1016/j.envsoft.2018.07.021 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_70 44.89 Endokrinologie VZ AR 108 2018 123-132 10 |
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Long term evolution of Molniya orbit under the effect of Earth’s non-spherical gravitational perturbation |
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Long term evolution of Molniya orbit under the effect of Earth’s non-spherical gravitational perturbation |
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Communicating physics-based wave model predictions of coral reefs using Bayesian belief networks |
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Communicating physics-based wave model predictions of coral reefs using Bayesian belief networks |
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Callaghan, David P. |
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Long term evolution of Molniya orbit under the effect of Earth’s non-spherical gravitational perturbation |
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Long term evolution of Molniya orbit under the effect of Earth’s non-spherical gravitational perturbation |
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communicating physics-based wave model predictions of coral reefs using bayesian belief networks |
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Communicating physics-based wave model predictions of coral reefs using Bayesian belief networks |
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The use of physics-based wave propagation predictions requires a considerable time commitment, a high level of expertise and extensive climate and reef data that are not always available when undertaking planning for management of coasts and coral reef ecosystems. Bayesian belief networks (BBNs) have at least three attributes that make them an excellent choice to communicate physics-based wave model predictions. First, BBNs subsume thousands of predictions to provide probabilistic outcomes. Second, by using prior probabilities, a practitioner can still obtain predictions of wave outcomes even when their knowledge of input parameters is incomplete. Third, BBNs can propagate evidence from outputs to inputs, which can be used to identify input conditions that are most likely to deliver a chosen outcome. These three attributes are tested and found to hold for a BBN developed for this purpose. |
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The use of physics-based wave propagation predictions requires a considerable time commitment, a high level of expertise and extensive climate and reef data that are not always available when undertaking planning for management of coasts and coral reef ecosystems. Bayesian belief networks (BBNs) have at least three attributes that make them an excellent choice to communicate physics-based wave model predictions. First, BBNs subsume thousands of predictions to provide probabilistic outcomes. Second, by using prior probabilities, a practitioner can still obtain predictions of wave outcomes even when their knowledge of input parameters is incomplete. Third, BBNs can propagate evidence from outputs to inputs, which can be used to identify input conditions that are most likely to deliver a chosen outcome. These three attributes are tested and found to hold for a BBN developed for this purpose. |
| abstract_unstemmed |
The use of physics-based wave propagation predictions requires a considerable time commitment, a high level of expertise and extensive climate and reef data that are not always available when undertaking planning for management of coasts and coral reef ecosystems. Bayesian belief networks (BBNs) have at least three attributes that make them an excellent choice to communicate physics-based wave model predictions. First, BBNs subsume thousands of predictions to provide probabilistic outcomes. Second, by using prior probabilities, a practitioner can still obtain predictions of wave outcomes even when their knowledge of input parameters is incomplete. Third, BBNs can propagate evidence from outputs to inputs, which can be used to identify input conditions that are most likely to deliver a chosen outcome. These three attributes are tested and found to hold for a BBN developed for this purpose. |
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Communicating physics-based wave model predictions of coral reefs using Bayesian belief networks |
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Baldock, Tom E. Shabani, Behnam Mumby, Peter J. |
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