Reclassification of rainfall regions of Turkey by K-means methodology and their temporal variability in relation to North Atlantic Oscillation (NAO)

Sönmez, İbrahim; Kömüşçü, Ali Ümran

doi:10.1007/s00704-011-0449-1

Cited by 32 publications

(16 citation statements)

References 33 publications

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“…Stooksbury and Michaels, ; Jackson and Weinand, ) and to analyse rainfall variability or distribution patterns (Sneyers et al , ; Ramos, ; Muñoz‐Diaz and Rodrigo, ; Dezfuli, ). Recently Sönmez and Kömüşcü () proposed a rainfall reclassification for Turkey based on k‐means methodology highlighting the benefit over prior techniques as regionalization based on topographic and climatic parameters, long‐term seasonal rainfall patterns (Türkeş et al , ) and hierarchical clustering (Ünal et al , ). Sönmez and Kömüşcü () in particular indicate that their method is efficient in grasping shifts in time periods.…”

Section: Introductionmentioning

confidence: 99%

“…Recently Sönmez and Kömüşcü () proposed a rainfall reclassification for Turkey based on k‐means methodology highlighting the benefit over prior techniques as regionalization based on topographic and climatic parameters, long‐term seasonal rainfall patterns (Türkeş et al , ) and hierarchical clustering (Ünal et al , ). Sönmez and Kömüşcü () in particular indicate that their method is efficient in grasping shifts in time periods. The advantage of using a cluster analysis in the regionalization procedure stands in the fact that rainfall time series may be non‐stationary and/or non‐Gaussian due to the complex of influence of climate phenomena (Takahashi and Dewitte, ).…”

Section: Introductionmentioning

confidence: 99%

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Regionalization of rainfall over the Peruvian Pacific slope and coast

et al. 2016

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Documenting the heterogeneity of rainfall regimes is a prerequisite for water resources management, mitigation of risks associated to extremes weather events and for impact studies. In this paper, we present a method for regionalization of rainfall over the Peruvian Pacific slope and coast, which is the main economic zone of the country and concentrates almost 50% of the population. Our approach is based on a two‐step process based on k‐means clustering followed by the regional vector method (RVM) applied to a network of 145 rainfall stations covering the period 1964–2011. The advantage of combining cluster analysis and RVM is demonstrated compared with just applying each of these methods. Nine homogeneous regions are identified that depict the salient features of the rainfall variability over the study area. A detailed characterization of the rainfall regime in each of the identified regions is presented in response to climate variability at seasonal and interannual timescale. They are shown to grasp the main modes of influence of the El Niño Southern Oscillation (ENSO), that is, increased rainfall over downstream regions in northern Peru during extreme El Niño events and decreased rainfall over upstream regions along the Pacific slope during central Pacific El Niño events. Overall our study points to the value of our two‐step regionalization procedure for climate impact studies.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Regionalization of rainfall over the Peruvian Pacific slope and coast

et al. 2016

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“…Hierarchical clustering methods begin with considering N objects as separate clusters, and the two objects with the most similarity strength are joined together under a new cluster in the next level. The clustering rule continues in the upper levels in the same manner until the final cluster contains all objects [1,3,[7][8].…”

Section: K-means Clusteringmentioning

confidence: 99%

“…The k-means algorithm is popular in finding clusters due to its simplicity of implementation. The objects are partitioned into k different clusters in the means that objects within each cluster are similar to each other as much as possible and as dissimilar as possible to the objects in other clusters [3,[7][8][9].…”

Section: K-means Clusteringmentioning

confidence: 99%

<title>Accident patterns for construction-related workers: a cluster analysis</title>

Liao

Tyan

2012

SPIE Proceedings

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The construction industry has been identified as one of the most hazardous industries. The risk of constructionrelated workers is far greater than that in a manufacturing based industry. However, some steps can be taken to reduce worker risk through effective injury prevention strategies. In this article, k-means clustering methodology is employed in specifying the factors related to different worker types and in identifying the patterns of industrial occupational accidents. Accident reports during the period 1998 to 2008 are extracted from case reports of the Northern Region Inspection Office of the Council of Labor Affairs of Taiwan. The results show that the cluster analysis can indicate some patterns of occupational injuries in the construction industry. Inspection plans should be proposed according to the type of construction-related workers. The findings provide a direction for more effective inspection strategies and injury prevention programs.

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“…Türkeş and Tatli (2011) identified eight consistent precipitation regime regions for the Anatolian Peninsula using spectral clustering for investigating atmospheric and geographical controls. Sönmez and Kömüşcü (2011) reclassified the rainfall regions of Turkey by using non-hierarchical K-means methodology and the relationship between drier or wetter periods and the North Atlantic Oscillation (NAO). They found six rainfall clusters, which included Aegean and Marmara regions in the same cluster, and determined that temporally dry periods are partially explained by NAO.…”

Section: Introductionmentioning

confidence: 99%