Aller au contenu principal
Accès ouvert déclaré 2024 conference-abstract

Evaluation of Flood Risk in South Korea Through Time Series Analysis of Precipitation Data

0Citations signalées, ce qui n’est pas une note de qualité
1Institutions déclarées
1Pays d’affiliation déclarés

Rattachement africain : kr. Niveau de preuve : code pays fourni par la source.

Le résumé fourni par la source

The recent increase in extreme and intense rainfall events is a pressing concern, mainly due to the effects of climate change. These heavy downpours contribute significantly to urban damage, triggering floods, landslides and other related disasters. Such natural disasters pose serious risks to human life and infrastructure. Seoul, South Korea, is particularly vulnerable. In 2001, for example, the city recorded a staggering 273 mm of rainfall in a single day. More recently, in 2011 and again in 2022, Seoul suffered extensive urban flooding. These events were largely due to the inability of rivers and stormwater systems to handle the sudden deluge, which exceeded the design frequencies that these infrastructures were originally built to withstand. As climate change accelerates, the frequency of such extreme weather events is expected to increase, requiring advanced research to fully understand their impact on urban infrastructure. This includes vital structures such as dams, embankments and stormwater pipes, all of which require robust designs to cope with such unprecedented environmental stress. This study focuses on collecting and analysing rainfall data from meteorological stations across Korea. By using the Generalised Extreme Value (GEV) distribution along with time series analysis, our research aims to meticulously map flood risk patterns. These statistical methods are crucial for understanding and predicting the behaviour of rare but severe weather phenomena. In addition, our research calculates flood loads based on specific return periods and design frequencies. These calculations are essential for designing infrastructure that can withstand future climatic challenges, thereby ensuring the safety and resilience of urban environments against the increasing threat of flooding. This comprehensive approach not only highlights historically flood-rich or flood-poor periods, but also assists in the strategic planning and upgrading of urban infrastructure to cope with the realities of climate change. AcknowledgementThis research was supported by a (2022-MOIS63-002(RS-2022-ND641012)) of Cooperative Research Method and Safety Management Technology in National Disaster funded by Ministry of Interior and Safety(MOIS, Korea).

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.

Le contrôle bibliographique ouvert

DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.

Titre Crossref
Evaluation of Flood Risk in South Korea Through Time Series Analysis of Precipitation Data
Date Crossref
16/08/2024
Éditeur
Copernicus GmbH
Type
posted-content

Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.

Où se fait cette recherche

  • Kangwon National University AI for Climate & Disaster Management Center pays non établi dans la notice
    Université ou école supérieure
  • Department of Artificial Intelligence & Software/Graduate School of Disaster Prevention pays non établi dans la notice
    Université ou école supérieure

AI for Climate & Disaster Management Center — Kangwon National University et Department of Artificial Intelligence & Software/Graduate School of Disaster Prevention.

Une affiliation ne permet pas de déduire la nationalité d’un auteur.

Les sujets associés

Flood Risk Assessment and Management

BNTIC News n’est pas le producteur de ces données. Les publications sont interrogées à la demande dans Crossref, OpenAIRE, DOAJ, Europe PMC, HAL, DataCite, AfricArXiv, ROR et la Banque mondiale, sans clé d’accès. OpenAlex reste optionnel. Aucun service payant n’est nécessaire et aucune donnée externe n’est enregistrée en base. Consulter les sources et leurs limites.