Identification of homogeneous rainfall regions in New South Wales, Australia

Identifying homogeneous regions based on spatial variables is vital for providing a certain and fixed region's spatial and temporal behavior. However, a significant problem of non-separation rises when the geographic coordinates are utilized for clustering, just because the Euclidean distance i...

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Autores principales:	Shahid Khan, Ijaz Hussain, Ataur Rahman
Formato:	article
Lenguaje:	EN
Publicado:	Taylor & Francis Group 2021
Materias:	new south wales precipitation partition around medoid clustering algorithm lambert projection method geographical coordinates lambert coordinates Oceanography GC1-1581 Meteorology. Climatology QC851-999
Acceso en línea:	https://doaj.org/article/73ecefe9fe0647909bb0b6f0e52b7932
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id	oai:doaj.org-article:73ecefe9fe0647909bb0b6f0e52b7932
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spelling	oai:doaj.org-article:73ecefe9fe0647909bb0b6f0e52b79322021-12-01T14:40:58ZIdentification of homogeneous rainfall regions in New South Wales, Australia1600-087010.1080/16000870.2021.1907979https://doaj.org/article/73ecefe9fe0647909bb0b6f0e52b79322021-01-01T00:00:00Zhttp://dx.doi.org/10.1080/16000870.2021.1907979https://doaj.org/toc/1600-0870Identifying homogeneous regions based on spatial variables is vital for providing a certain and fixed region's spatial and temporal behavior. However, a significant problem of non-separation rises when the geographic coordinates are utilized for clustering, just because the Euclidean distance is not suitable for clustering when considering the geographic coordinates. Therefore, this study focuses on employing such methods where the non-separation is minimum for identifying homogenous regions. The average annual rainfall data of 226 meteorological monitoring stations for 1911–2018 of New South Wales (NSW), Australia, was considered for the current study. The data is standardized with zero mean and unit variance to remove the effect of different measurement scales. The geographical coordinates are then converted to rectangular coordinates by the Lambert projection method. Using the Partition Around Medoid (PAM) algorithm, also known as the k-medoid algorithm (which minimizes the sum of dissimilarities instead of the sum of squares of Euclidean distances) on rectangular Lambert projected coordinates, 10 well-separated clusters are obtained. The Mean Squared Prediction Error (MSPE) is comparatively smaller if the prediction of unobserved locations in cluster 3 is made. However, this error increases if the prediction is made for a complete monitoring network. The identified 10 homogeneous regions or clusters provide a good separation when the lambert coordinates are used instead of geographical coordinates.Shahid KhanIjaz HussainAtaur RahmanTaylor & Francis Grouparticlenew south walesprecipitationpartition around medoid clustering algorithmlambert projection methodgeographical coordinateslambert coordinatesOceanographyGC1-1581Meteorology. ClimatologyQC851-999ENTellus: Series A, Dynamic Meteorology and Oceanography, Vol 73, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	new south wales precipitation partition around medoid clustering algorithm lambert projection method geographical coordinates lambert coordinates Oceanography GC1-1581 Meteorology. Climatology QC851-999
spellingShingle	new south wales precipitation partition around medoid clustering algorithm lambert projection method geographical coordinates lambert coordinates Oceanography GC1-1581 Meteorology. Climatology QC851-999 Shahid Khan Ijaz Hussain Ataur Rahman Identification of homogeneous rainfall regions in New South Wales, Australia
description	Identifying homogeneous regions based on spatial variables is vital for providing a certain and fixed region's spatial and temporal behavior. However, a significant problem of non-separation rises when the geographic coordinates are utilized for clustering, just because the Euclidean distance is not suitable for clustering when considering the geographic coordinates. Therefore, this study focuses on employing such methods where the non-separation is minimum for identifying homogenous regions. The average annual rainfall data of 226 meteorological monitoring stations for 1911–2018 of New South Wales (NSW), Australia, was considered for the current study. The data is standardized with zero mean and unit variance to remove the effect of different measurement scales. The geographical coordinates are then converted to rectangular coordinates by the Lambert projection method. Using the Partition Around Medoid (PAM) algorithm, also known as the k-medoid algorithm (which minimizes the sum of dissimilarities instead of the sum of squares of Euclidean distances) on rectangular Lambert projected coordinates, 10 well-separated clusters are obtained. The Mean Squared Prediction Error (MSPE) is comparatively smaller if the prediction of unobserved locations in cluster 3 is made. However, this error increases if the prediction is made for a complete monitoring network. The identified 10 homogeneous regions or clusters provide a good separation when the lambert coordinates are used instead of geographical coordinates.
format	article
author	Shahid Khan Ijaz Hussain Ataur Rahman
author_facet	Shahid Khan Ijaz Hussain Ataur Rahman
author_sort	Shahid Khan
title	Identification of homogeneous rainfall regions in New South Wales, Australia
title_short	Identification of homogeneous rainfall regions in New South Wales, Australia
title_full	Identification of homogeneous rainfall regions in New South Wales, Australia
title_fullStr	Identification of homogeneous rainfall regions in New South Wales, Australia
title_full_unstemmed	Identification of homogeneous rainfall regions in New South Wales, Australia
title_sort	identification of homogeneous rainfall regions in new south wales, australia
publisher	Taylor & Francis Group
publishDate	2021
url	https://doaj.org/article/73ecefe9fe0647909bb0b6f0e52b7932
work_keys_str_mv	AT shahidkhan identificationofhomogeneousrainfallregionsinnewsouthwalesaustralia AT ijazhussain identificationofhomogeneousrainfallregionsinnewsouthwalesaustralia AT ataurrahman identificationofhomogeneousrainfallregionsinnewsouthwalesaustralia
_version_	1718405000151957504

Identification of homogeneous rainfall regions in New South Wales, Australia

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