Rainfall Patterns over India: Classification with Fuzzy c-Means Method
Summary Seasonal (June through September) percentage departure from normal rainfall patterns over India for the period 1871–1994 have been classified using Fuzzy c-means method (FCM) to identify the dominant modes of spatio-temporal variability in the Indian monsoon rainfall. Unlike the hard cluster...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Kulkarni, A. [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
1998 |
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| Schlagwörter: |
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| Systematik: |
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| Anmerkung: |
© Springer-Verlag/Wien 1998 |
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| Übergeordnetes Werk: |
Enthalten in: Theoretical and applied climatology - Springer-Verlag, 1986, 59(1998), 3-4 vom: Mai, Seite 137-146 |
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| Übergeordnetes Werk: |
volume:59 ; year:1998 ; number:3-4 ; month:05 ; pages:137-146 |
| Links: |
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| DOI / URN: |
10.1007/s007040050019 |
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| Katalog-ID: |
OLC2048435270 |
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| 520 | |a Summary Seasonal (June through September) percentage departure from normal rainfall patterns over India for the period 1871–1994 have been classified using Fuzzy c-means method (FCM) to identify the dominant modes of spatio-temporal variability in the Indian monsoon rainfall. Unlike the hard clustering methods, for example the Map-to-Map (MM) correlation method and the k-means (KM) clustering method, this method does not force a pattern to get classified into only one cluster but assigns varying membership to every cluster. Thus marginal patterns get classified into all clusters with different memberships. Patterns for the 124-year period are represented by the four dominant clusters. The spatial patterns associated with the extreme (deficient/excess) Indian monsoon rainfall (IMR) get high membership in one of the clusters only, while the patterns associated with the normal IMR get almost equal membership to all clusters. Even the spatial patterns during the El Niño/La Nina episodes show high preference to a particular cluster. Time variation of each cluster shows that there are epochs where a particular cluster dominates. Possible dynamic causes leading to the clusters are examined. Merits and demerits of the FCM method are also discussed. | ||
| 650 | 4 | |a India | |
| 650 | 4 | |a Spatial Pattern | |
| 650 | 4 | |a Time Variation | |
| 650 | 4 | |a Cluster Method | |
| 650 | 4 | |a Percentage Departure | |
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10.1007/s007040050019 doi (DE-627)OLC2048435270 (DE-He213)s007040050019-p DE-627 ger DE-627 rakwb eng 550 VZ 14 ssgn RA 1000 VZ rvk Kulkarni, A. verfasserin aut Rainfall Patterns over India: Classification with Fuzzy c-Means Method 1998 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag/Wien 1998 Summary Seasonal (June through September) percentage departure from normal rainfall patterns over India for the period 1871–1994 have been classified using Fuzzy c-means method (FCM) to identify the dominant modes of spatio-temporal variability in the Indian monsoon rainfall. Unlike the hard clustering methods, for example the Map-to-Map (MM) correlation method and the k-means (KM) clustering method, this method does not force a pattern to get classified into only one cluster but assigns varying membership to every cluster. Thus marginal patterns get classified into all clusters with different memberships. Patterns for the 124-year period are represented by the four dominant clusters. The spatial patterns associated with the extreme (deficient/excess) Indian monsoon rainfall (IMR) get high membership in one of the clusters only, while the patterns associated with the normal IMR get almost equal membership to all clusters. Even the spatial patterns during the El Niño/La Nina episodes show high preference to a particular cluster. Time variation of each cluster shows that there are epochs where a particular cluster dominates. Possible dynamic causes leading to the clusters are examined. Merits and demerits of the FCM method are also discussed. India Spatial Pattern Time Variation Cluster Method Percentage Departure Kripalani, R. H. aut Enthalten in Theoretical and applied climatology Springer-Verlag, 1986 59(1998), 3-4 vom: Mai, Seite 137-146 (DE-627)129958808 (DE-600)405799-5 (DE-576)01552857X 0177-798X nnns volume:59 year:1998 number:3-4 month:05 pages:137-146 https://doi.org/10.1007/s007040050019 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GGO GBV_ILN_11 GBV_ILN_22 GBV_ILN_40 GBV_ILN_70 GBV_ILN_130 GBV_ILN_601 GBV_ILN_2001 GBV_ILN_2006 GBV_ILN_2010 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4037 GBV_ILN_4306 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4325 RA 1000 AR 59 1998 3-4 05 137-146 |
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10.1007/s007040050019 doi (DE-627)OLC2048435270 (DE-He213)s007040050019-p DE-627 ger DE-627 rakwb eng 550 VZ 14 ssgn RA 1000 VZ rvk Kulkarni, A. verfasserin aut Rainfall Patterns over India: Classification with Fuzzy c-Means Method 1998 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag/Wien 1998 Summary Seasonal (June through September) percentage departure from normal rainfall patterns over India for the period 1871–1994 have been classified using Fuzzy c-means method (FCM) to identify the dominant modes of spatio-temporal variability in the Indian monsoon rainfall. Unlike the hard clustering methods, for example the Map-to-Map (MM) correlation method and the k-means (KM) clustering method, this method does not force a pattern to get classified into only one cluster but assigns varying membership to every cluster. Thus marginal patterns get classified into all clusters with different memberships. Patterns for the 124-year period are represented by the four dominant clusters. The spatial patterns associated with the extreme (deficient/excess) Indian monsoon rainfall (IMR) get high membership in one of the clusters only, while the patterns associated with the normal IMR get almost equal membership to all clusters. Even the spatial patterns during the El Niño/La Nina episodes show high preference to a particular cluster. Time variation of each cluster shows that there are epochs where a particular cluster dominates. Possible dynamic causes leading to the clusters are examined. Merits and demerits of the FCM method are also discussed. India Spatial Pattern Time Variation Cluster Method Percentage Departure Kripalani, R. H. aut Enthalten in Theoretical and applied climatology Springer-Verlag, 1986 59(1998), 3-4 vom: Mai, Seite 137-146 (DE-627)129958808 (DE-600)405799-5 (DE-576)01552857X 0177-798X nnns volume:59 year:1998 number:3-4 month:05 pages:137-146 https://doi.org/10.1007/s007040050019 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GGO GBV_ILN_11 GBV_ILN_22 GBV_ILN_40 GBV_ILN_70 GBV_ILN_130 GBV_ILN_601 GBV_ILN_2001 GBV_ILN_2006 GBV_ILN_2010 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4037 GBV_ILN_4306 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4325 RA 1000 AR 59 1998 3-4 05 137-146 |
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10.1007/s007040050019 doi (DE-627)OLC2048435270 (DE-He213)s007040050019-p DE-627 ger DE-627 rakwb eng 550 VZ 14 ssgn RA 1000 VZ rvk Kulkarni, A. verfasserin aut Rainfall Patterns over India: Classification with Fuzzy c-Means Method 1998 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag/Wien 1998 Summary Seasonal (June through September) percentage departure from normal rainfall patterns over India for the period 1871–1994 have been classified using Fuzzy c-means method (FCM) to identify the dominant modes of spatio-temporal variability in the Indian monsoon rainfall. Unlike the hard clustering methods, for example the Map-to-Map (MM) correlation method and the k-means (KM) clustering method, this method does not force a pattern to get classified into only one cluster but assigns varying membership to every cluster. Thus marginal patterns get classified into all clusters with different memberships. Patterns for the 124-year period are represented by the four dominant clusters. The spatial patterns associated with the extreme (deficient/excess) Indian monsoon rainfall (IMR) get high membership in one of the clusters only, while the patterns associated with the normal IMR get almost equal membership to all clusters. Even the spatial patterns during the El Niño/La Nina episodes show high preference to a particular cluster. Time variation of each cluster shows that there are epochs where a particular cluster dominates. Possible dynamic causes leading to the clusters are examined. Merits and demerits of the FCM method are also discussed. India Spatial Pattern Time Variation Cluster Method Percentage Departure Kripalani, R. H. aut Enthalten in Theoretical and applied climatology Springer-Verlag, 1986 59(1998), 3-4 vom: Mai, Seite 137-146 (DE-627)129958808 (DE-600)405799-5 (DE-576)01552857X 0177-798X nnns volume:59 year:1998 number:3-4 month:05 pages:137-146 https://doi.org/10.1007/s007040050019 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GGO GBV_ILN_11 GBV_ILN_22 GBV_ILN_40 GBV_ILN_70 GBV_ILN_130 GBV_ILN_601 GBV_ILN_2001 GBV_ILN_2006 GBV_ILN_2010 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4037 GBV_ILN_4306 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4325 RA 1000 AR 59 1998 3-4 05 137-146 |
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10.1007/s007040050019 doi (DE-627)OLC2048435270 (DE-He213)s007040050019-p DE-627 ger DE-627 rakwb eng 550 VZ 14 ssgn RA 1000 VZ rvk Kulkarni, A. verfasserin aut Rainfall Patterns over India: Classification with Fuzzy c-Means Method 1998 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag/Wien 1998 Summary Seasonal (June through September) percentage departure from normal rainfall patterns over India for the period 1871–1994 have been classified using Fuzzy c-means method (FCM) to identify the dominant modes of spatio-temporal variability in the Indian monsoon rainfall. Unlike the hard clustering methods, for example the Map-to-Map (MM) correlation method and the k-means (KM) clustering method, this method does not force a pattern to get classified into only one cluster but assigns varying membership to every cluster. Thus marginal patterns get classified into all clusters with different memberships. Patterns for the 124-year period are represented by the four dominant clusters. The spatial patterns associated with the extreme (deficient/excess) Indian monsoon rainfall (IMR) get high membership in one of the clusters only, while the patterns associated with the normal IMR get almost equal membership to all clusters. Even the spatial patterns during the El Niño/La Nina episodes show high preference to a particular cluster. Time variation of each cluster shows that there are epochs where a particular cluster dominates. Possible dynamic causes leading to the clusters are examined. Merits and demerits of the FCM method are also discussed. India Spatial Pattern Time Variation Cluster Method Percentage Departure Kripalani, R. H. aut Enthalten in Theoretical and applied climatology Springer-Verlag, 1986 59(1998), 3-4 vom: Mai, Seite 137-146 (DE-627)129958808 (DE-600)405799-5 (DE-576)01552857X 0177-798X nnns volume:59 year:1998 number:3-4 month:05 pages:137-146 https://doi.org/10.1007/s007040050019 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GGO GBV_ILN_11 GBV_ILN_22 GBV_ILN_40 GBV_ILN_70 GBV_ILN_130 GBV_ILN_601 GBV_ILN_2001 GBV_ILN_2006 GBV_ILN_2010 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4037 GBV_ILN_4306 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4325 RA 1000 AR 59 1998 3-4 05 137-146 |
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10.1007/s007040050019 doi (DE-627)OLC2048435270 (DE-He213)s007040050019-p DE-627 ger DE-627 rakwb eng 550 VZ 14 ssgn RA 1000 VZ rvk Kulkarni, A. verfasserin aut Rainfall Patterns over India: Classification with Fuzzy c-Means Method 1998 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag/Wien 1998 Summary Seasonal (June through September) percentage departure from normal rainfall patterns over India for the period 1871–1994 have been classified using Fuzzy c-means method (FCM) to identify the dominant modes of spatio-temporal variability in the Indian monsoon rainfall. Unlike the hard clustering methods, for example the Map-to-Map (MM) correlation method and the k-means (KM) clustering method, this method does not force a pattern to get classified into only one cluster but assigns varying membership to every cluster. Thus marginal patterns get classified into all clusters with different memberships. Patterns for the 124-year period are represented by the four dominant clusters. The spatial patterns associated with the extreme (deficient/excess) Indian monsoon rainfall (IMR) get high membership in one of the clusters only, while the patterns associated with the normal IMR get almost equal membership to all clusters. Even the spatial patterns during the El Niño/La Nina episodes show high preference to a particular cluster. Time variation of each cluster shows that there are epochs where a particular cluster dominates. Possible dynamic causes leading to the clusters are examined. Merits and demerits of the FCM method are also discussed. India Spatial Pattern Time Variation Cluster Method Percentage Departure Kripalani, R. H. aut Enthalten in Theoretical and applied climatology Springer-Verlag, 1986 59(1998), 3-4 vom: Mai, Seite 137-146 (DE-627)129958808 (DE-600)405799-5 (DE-576)01552857X 0177-798X nnns volume:59 year:1998 number:3-4 month:05 pages:137-146 https://doi.org/10.1007/s007040050019 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-GEO SSG-OPC-GGO GBV_ILN_11 GBV_ILN_22 GBV_ILN_40 GBV_ILN_70 GBV_ILN_130 GBV_ILN_601 GBV_ILN_2001 GBV_ILN_2006 GBV_ILN_2010 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4029 GBV_ILN_4037 GBV_ILN_4306 GBV_ILN_4311 GBV_ILN_4313 GBV_ILN_4325 RA 1000 AR 59 1998 3-4 05 137-146 |
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Kulkarni, A. |
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550 VZ 14 ssgn RA 1000 VZ rvk Rainfall Patterns over India: Classification with Fuzzy c-Means Method India Spatial Pattern Time Variation Cluster Method Percentage Departure |
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Rainfall Patterns over India: Classification with Fuzzy c-Means Method |
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Rainfall Patterns over India: Classification with Fuzzy c-Means Method |
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Theoretical and applied climatology |
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1998 |
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Kulkarni, A. Kripalani, R. H. |
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rainfall patterns over india: classification with fuzzy c-means method |
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Rainfall Patterns over India: Classification with Fuzzy c-Means Method |
| abstract |
Summary Seasonal (June through September) percentage departure from normal rainfall patterns over India for the period 1871–1994 have been classified using Fuzzy c-means method (FCM) to identify the dominant modes of spatio-temporal variability in the Indian monsoon rainfall. Unlike the hard clustering methods, for example the Map-to-Map (MM) correlation method and the k-means (KM) clustering method, this method does not force a pattern to get classified into only one cluster but assigns varying membership to every cluster. Thus marginal patterns get classified into all clusters with different memberships. Patterns for the 124-year period are represented by the four dominant clusters. The spatial patterns associated with the extreme (deficient/excess) Indian monsoon rainfall (IMR) get high membership in one of the clusters only, while the patterns associated with the normal IMR get almost equal membership to all clusters. Even the spatial patterns during the El Niño/La Nina episodes show high preference to a particular cluster. Time variation of each cluster shows that there are epochs where a particular cluster dominates. Possible dynamic causes leading to the clusters are examined. Merits and demerits of the FCM method are also discussed. © Springer-Verlag/Wien 1998 |
| abstractGer |
Summary Seasonal (June through September) percentage departure from normal rainfall patterns over India for the period 1871–1994 have been classified using Fuzzy c-means method (FCM) to identify the dominant modes of spatio-temporal variability in the Indian monsoon rainfall. Unlike the hard clustering methods, for example the Map-to-Map (MM) correlation method and the k-means (KM) clustering method, this method does not force a pattern to get classified into only one cluster but assigns varying membership to every cluster. Thus marginal patterns get classified into all clusters with different memberships. Patterns for the 124-year period are represented by the four dominant clusters. The spatial patterns associated with the extreme (deficient/excess) Indian monsoon rainfall (IMR) get high membership in one of the clusters only, while the patterns associated with the normal IMR get almost equal membership to all clusters. Even the spatial patterns during the El Niño/La Nina episodes show high preference to a particular cluster. Time variation of each cluster shows that there are epochs where a particular cluster dominates. Possible dynamic causes leading to the clusters are examined. Merits and demerits of the FCM method are also discussed. © Springer-Verlag/Wien 1998 |
| abstract_unstemmed |
Summary Seasonal (June through September) percentage departure from normal rainfall patterns over India for the period 1871–1994 have been classified using Fuzzy c-means method (FCM) to identify the dominant modes of spatio-temporal variability in the Indian monsoon rainfall. Unlike the hard clustering methods, for example the Map-to-Map (MM) correlation method and the k-means (KM) clustering method, this method does not force a pattern to get classified into only one cluster but assigns varying membership to every cluster. Thus marginal patterns get classified into all clusters with different memberships. Patterns for the 124-year period are represented by the four dominant clusters. The spatial patterns associated with the extreme (deficient/excess) Indian monsoon rainfall (IMR) get high membership in one of the clusters only, while the patterns associated with the normal IMR get almost equal membership to all clusters. Even the spatial patterns during the El Niño/La Nina episodes show high preference to a particular cluster. Time variation of each cluster shows that there are epochs where a particular cluster dominates. Possible dynamic causes leading to the clusters are examined. Merits and demerits of the FCM method are also discussed. © Springer-Verlag/Wien 1998 |
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Rainfall Patterns over India: Classification with Fuzzy c-Means Method |
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