You are hereHome | Optimal temporal distribution curves for the classification of heavy precipitation using hierarchical clustering on principal components
Optimal temporal distribution curves for the classification of heavy precipitation using hierarchical clustering on principal componentsView Crossref metadata(active tab) <div id="edit-xml" class="form-item form-type-item"> <?xml version="1.0"?> <doi_batch version="4.4.1" xmlns="http://www.crossref.org/schema/4.4.1" xmlns:jats="http://www.ncbi.nlm.nih.gov/JATS1" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.crossref.org/schema/4.4.1 http://www.crossref.org/schemas/crossref4.4.1.xsd"><head><doi_batch_id>000689</doi_batch_id><timestamp>1713441883</timestamp><depositor><depositor_name>University of the Aegean</depositor_name><email_address>uoa@university.com</email_address></depositor><registrant>Global NEST Journal</registrant></head><body><journal><journal_metadata><full_title>Global NEST Journal</full_title><full_title>Global NEST: the international Journal</full_title><issn media_type="print">1790-7632</issn><issn media_type="electronic">2241-777X</issn></journal_metadata><journal_issue><titles><title>Issue 4</title></titles><publication_date><month>12</month><year>2024</year></publication_date><journal_volume><volume>21</volume></journal_volume><issue>4</issue></journal_issue><journal_article><titles><title>Optimal temporal distribution curves for the classification of heavy precipitation using hierarchical clustering on principal components</title></titles><abstract xmlns="http://www.ncbi.nlm.nih.gov/JATS1"><p><p>A novel method that utilizes a combination of statistical and clustering techniques is presented in order to classify statistically independent heavy rainstorm events and create a limited number of temporal distribution curves. These curves represent the centers of many unitless cumulative rainstorm events and express the temporal distribution patterns in a probabilistic way. The whole process includes the necessary steps from importing raw precipitation time series data to producing the initially unknown optimal number of representative curves. These hyetographs can be used for stochastic simulation, water resources planning, water quality assessment and global change studying. The present type of analysis is fully unsupervised, as no empirical knowledge of local rainfalls is implicated or any arbitrary introduction of quartiles for grouping as is the case in the pertinent literature. An example using data from a Greek Water Division illustrates that the proposed method produces clusters with superior internal structure and temporal distribution curves that are not coming from the same distribution, in contrast to the results using Huff’s curves classification.</p> </p></abstract><publication_date><month>09</month><day>09</day><year>2019</year></publication_date><pages><first_page>530</first_page><last_page>538</last_page></pages><publisher_item><identifier id_type="pii">gnest_02997</identifier></publisher_item><doi_data><doi>10.30955/gnj.002997</doi><timestamp>1713441883</timestamp><resource>https://journal.gnest.org/publication/gnest_02997</resource></doi_data></journal_article></journal></body></doi_batch> </div>
