Dataset Open Access

Flood Modeling Dataset 3

Chang Wei Tan; Christoph Bergmeir; Francois Petitjean; Geoffrey I Webb


Dublin Core Export

<?xml version='1.0' encoding='utf-8'?>
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Chang Wei Tan</dc:creator>
  <dc:creator>Christoph Bergmeir</dc:creator>
  <dc:creator>Francois Petitjean</dc:creator>
  <dc:creator>Geoffrey I Webb</dc:creator>
  <dc:date>2020-06-21</dc:date>
  <dc:description>This dataset is part of the Monash, UEA &amp; UCR time series regression repository. http://tseregression.org/

The goal of this dataset is to predict maximum water depth for flood modelling. The dataset contains 613hourly rainfall events time series which are used to predict the maximum water depth of a domain (Digital Elevation Model, DEM). The rainfall events and DEM are generated synthetically by researchers at Monash University because real DEM data with accurate rainfall events are rare. 

Data Donor
Jihane Elyahyioui, jihane.elyahyioui@monash.edu</dc:description>
  <dc:identifier>https://zenodo.org/record/3902698</dc:identifier>
  <dc:identifier>10.5281/zenodo.3902698</dc:identifier>
  <dc:identifier>oai:zenodo.org:3902698</dc:identifier>
  <dc:relation>doi:10.5281/zenodo.3902697</dc:relation>
  <dc:relation>url:https://zenodo.org/communities/ts_regression</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:subject>time series</dc:subject>
  <dc:subject>regression</dc:subject>
  <dc:title>Flood Modeling Dataset 3</dc:title>
  <dc:type>info:eu-repo/semantics/other</dc:type>
  <dc:type>dataset</dc:type>
</oai_dc:dc>
144
119
views
downloads
All versions This version
Views 144144
Downloads 119119
Data volume 68.7 MB68.7 MB
Unique views 131131
Unique downloads 7070

Share

Cite as