Identification of Homogeneous Rainfall Regimes in Northeast Region of India using Fuzzy Cluster Analysis

Regionalization methods are often used in hydrology for frequency analysis of floods. The hydrologically homogeneous regions should be determined using cluster analysis instead of the geographically close stations. In view of the ongoing environmental and climate changes in the Northeastern of India...

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Vydané v:	Water resources management Ročník 28; číslo 13; s. 4491 - 4511
Hlavní autori:	Goyal, Manish Kumar, Gupta, Vivek
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Dordrecht Springer-Verlag 01.10.2014 Springer Netherlands Springer Springer Nature B.V
Predmet:	Algorithms Atmospheric Sciences basins Civil Engineering Climate change Cluster analysis Clustering Correlation analysis Earth and Environmental Science Earth Sciences Earth, ocean, space Engineering and environment geology. Geothermics Environment Exact sciences and technology Flood frequency Floods Forecasting Frequency analysis Freshwater Fuzzy Geotechnical Engineering & Applied Earth Sciences Homogeneity Hydrogeology Hydrologic data Hydrology Hydrology. Hydrogeology Hydrology/Water Resources India Natural hazards: prediction, damages, etc Partitions Precipitation rain Regional Regions Studies Validity Water resources Water resources management India Asia Clustering Regionalization Flood frequency analysis algorithms floods partition coefficients rainfall atmospheric precipitation frequency homogeneity entropy water resource management cluster analysis methodology climate change
ISSN:	0920-4741, 1573-1650
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Abstract	Regionalization methods are often used in hydrology for frequency analysis of floods. The hydrologically homogeneous regions should be determined using cluster analysis instead of the geographically close stations. In view of the ongoing environmental and climate changes in the Northeastern of India, regionalization of homogeneous rainfall region is essential to lay out an effective flood frequency analysis of this region. The choice of appropriate cluster approach used according to the data of the basin is also significant. In the context of this study, total precipitation data of stations operated by Indian Meteorological Department (IMD) in Northeastern of India basins for cluster analysis are used. Further, five cluster validity indices, namely Partition Coefficient, Partition Entropy, Extended Xie-Beni index, Fukuyama-Sugeno index and Kwon index have been tested to determine the effectiveness in identifying optimal partition provided by the fuzzy c mean clustering algorithm (FCM). A comparison is also performed using K- Mean clustering algorithm. Additionally, regional homogeneity tests based on L-moments approach are used to check homogeneity of regions identified by both cluster analysis approaches. It was concluded that regional homogeneity test results show that regions defined by FCM method are sufficiently homogeneous for regional frequency analysis.
AbstractList	Regionalization methods are often used in hydrology for frequency analysis of floods. The hydrologically homogeneous regions should be determined using cluster analysis instead of the geographically close stations. In view of the ongoing environmental and climate changes in the Northeastern of India, regionalization of homogeneous rainfall region is essential to lay out an effective flood frequency analysis of this region. The choice of appropriate cluster approach used according to the data of the basin is also significant. In the context of this study, total precipitation data of stations operated by Indian Meteorological Department (IMD) in Northeastern of India basins for cluster analysis are used. Further, five cluster validity indices, namely Partition Coefficient, Partition Entropy, Extended Xie-Beni index, Fukuyama-Sugeno index and Kwon index have been tested to determine the effectiveness in identifying optimal partition provided by the fuzzy c mean clustering algorithm (FCM). A comparison is also performed using K- Mean clustering algorithm. Additionally, regional homogeneity tests based on l-moments approach are used to check homogeneity of regions identified by both cluster analysis approaches. It was concluded that regional homogeneity test results show that regions defined by FCM method are sufficiently homogeneous for regional frequency analysis. Regionalization methods are often used in hydrology for frequency analysis of floods. The hydrologically homogeneous regions should be determined using cluster analysis instead of the geographically close stations. In view of the ongoing environmental and climate changes in the Northeastern of India, regionalization of homogeneous rainfall region is essential to lay out an effective flood frequency analysis of this region. The choice of appropriate cluster approach used according to the data of the basin is also significant. In the context of this study, total precipitation data of stations operated by Indian Meteorological Department (IMD) in Northeastern of India basins for cluster analysis are used. Further, five cluster validity indices, namely Partition Coefficient, Partition Entropy, Extended Xie-Beni index, Fukuyama-Sugeno index and Kwon index have been tested to determine the effectiveness in identifying optimal partition provided by the fuzzy c mean clustering algorithm (FCM). A comparison is also performed using K - Mean clustering algorithm. Additionally, regional homogeneity tests based on l -moments approach are used to check homogeneity of regions identified by both cluster analysis approaches. It was concluded that regional homogeneity test results show that regions defined by FCM method are sufficiently homogeneous for regional frequency analysis. Regionalization methods are often used in hydrology for frequency analysis of floods. The hydrologically homogeneous regions should be determined using cluster analysis instead of the geographically close stations. In view of the ongoing environmental and climate changes in the Northeastern of India, regionalization of homogeneous rainfall region is essential to lay out an effective flood frequency analysis of this region. The choice of appropriate cluster approach used according to the data of the basin is also significant. In the context of this study, total precipitation data of stations operated by Indian Meteorological Department (IMD) in Northeastern of India basins for cluster analysis are used. Further, five cluster validity indices, namely Partition Coefficient, Partition Entropy, Extended Xie-Beni index, Fukuyama-Sugeno index and Kwon index have been tested to determine the effectiveness in identifying optimal partition provided by the fuzzy c mean clustering algorithm (FCM). A comparison is also performed using K- Mean clustering algorithm. Additionally, regional homogeneity tests based on l-moments approach are used to check homogeneity of regions identified by both cluster analysis approaches. It was concluded that regional homogeneity test results show that regions defined by FCM method are sufficiently homogeneous for regional frequency analysis.[PUBLICATION ABSTRACT]
Author	Gupta, Vivek Goyal, Manish Kumar
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Keywords	Clustering Regionalization Flood frequency analysis algorithms floods partition coefficients rainfall atmospheric precipitation frequency homogeneity entropy water resource management cluster analysis methodology climate change
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Paper presented to the annual conference of the Canadian society of civil engineering1989NewfoundlandSt. John’s HoskingJRML-moments Analysis and estimation of distributions using linear combinations of order statisticsJ R Stat Soc Ser B Methodol1990521105124 RaoRASrinivasVVRegionalization of watersheds by hybrid-cluster analysisJ Hydrol2006a318375610.1016/j.jhydrol.2005.06.004 BurnDHAn appraisal of the ‘region of influence’ approach to flood frequency analysisHydrol Sci J1990a35214916510.1080/02626669009492415 LimYHVoellerDLRegional flood estimations in Red river using L-moment-based index-flood and bulletin 17B proceduresJ Hydrol Eng20091491002101610.1061/(ASCE)HE.1943-5584.0000102 HalkidiMBatistakisYVazirgiannisMOn clustering validation techniquesJ Intell Inf Syst20011710714510.1023/A:1012801612483 Beable M E, McKercher A I (1982) Regional flood estimation in New Zealand. Water and Soil Technical Publication 20, Ministry of works and development, Wellington. 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Natural environment Research Council London SrinivasaRKNageshKDClassification of Indian meteorological stations using cluster and fuzzy cluster analysis, and Kohonen artificial neural networksNord Hydrol200738330331410.2166/nh.2007.013 DashSKNehaSKCPattnayakXJGaoYSTemperature and precipitation changes in the north-east India and their future projectionsGlob Planet Chang201298–9912314410.1016/j.gloplacha.2012.07.006 DunnJCA fuzzy relative of the ISODATA process and its Use in detecting compact, well separated clustersJournal Cybern19743325710.1080/01969727308546046 GreenwoodJALandwehrJMMatalasNCWallisJRProbability-weighted moments: definition and relation to parameters of several distributions expressable in inverse formWater Resour Res1979151049105410.1029/WR015i005p01049 LinGFChenLHA reliability-based selective index for regional flood frequency analysis methods using the Self-organizing MapHydrol Process200317132653266310.1002/hyp.1267 KulkarniAKripalaniRHRainfall patterns over India: classification with fuzzy c-means methodTheor Appl Climatol19985913714610.1007/s007040050019 DH Burn (699_CR9) 1990; 26 GS Cavadias (699_CR13) 1990 JC Bezdek (699_CR5) 1984; 10 RK Srinivasa (699_CR40) 2007; 38 JC Bezdek (699_CR2) 1974; 1 699_CR41 M Cannarozzo (699_CR11) 2009; 34 DL Davies (699_CR17) 1979; PAMI-1 699_CR21 GS Cavadias (699_CR12) 1989 JC Bezdek (699_CR4) 1981 699_CR22 SH Kwon (699_CR30) 1998; 34 JRM Hosking (699_CR27) 1993; 29 RA Rao (699_CR37) 2006a; 318 699_CR1 S Gabriele (699_CR23) 1991; 27 A Das (699_CR15) 2009 JRM Hosking (699_CR26) 1990; 52 JRM Hosking (699_CR28) 1997 PJ Rousseeuw (699_CR39) 1987; 20 DH Burn (699_CR10) 1997; 202 SK Dash (699_CR16) 2012; 98–99 F Dikbas (699_CR18) 2012; 32 JA Greenwood (699_CR24) 1979; 15 DH Burn (699_CR8) 1990a; 35 NC Matlas (699_CR34) 1975; 11 YH Lim (699_CR31) 2009; 14 JC Bezdek (699_CR3) 1974; 3 J Dunn (699_CR20) 1974; 4 RA Rao (699_CR38) 2006; 318 B Bobee (699_CR7) 1995; 33 A Kulkarni (699_CR29) 1998; 59 J Macqueen (699_CR33) 1967 699_CR14 JC Dunn (699_CR19) 1974; 3 NR Pal (699_CR36) 1995; 3 B Venkatesh (699_CR42) 2007; 116 NR Bhaskar (699_CR6) 1989; 115 699_CR35 XL Xie (699_CR43) 1991; 13 M Halkidi (699_CR25) 2001; 17 GF Lin (699_CR32) 2003; 17
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Snippet	Regionalization methods are often used in hydrology for frequency analysis of floods. The hydrologically homogeneous regions should be determined using cluster...
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SubjectTerms	Algorithms Atmospheric Sciences basins Civil Engineering Climate change Cluster analysis Clustering Correlation analysis Earth and Environmental Science Earth Sciences Earth, ocean, space Engineering and environment geology. Geothermics Environment Exact sciences and technology Flood frequency Floods Forecasting Frequency analysis Freshwater Fuzzy Geotechnical Engineering & Applied Earth Sciences Homogeneity Hydrogeology Hydrologic data Hydrology Hydrology. Hydrogeology Hydrology/Water Resources India Natural hazards: prediction, damages, etc Partitions Precipitation rain Regional Regions Studies Validity Water resources Water resources management
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Title	Identification of Homogeneous Rainfall Regimes in Northeast Region of India using Fuzzy Cluster Analysis
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