Next Article in Journal
The Relationship between Effective and Equitable Water Allocation, Local Rice Farmer Participation and Economic Well-Being: Insights from Thailand’s Chiang Mai Province
Previous Article in Journal
Prediction of Iron Release during Riverbank Filtration
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessArticle
Water 2017, 9(5), 318; doi:10.3390/w9050318

Evaluating Flood Exposure for Properties in Urban Areas Using a Multivariate Modelling Technique

1
Faculty of Science and Technology, Norwegian University of Life Sciences, 1430 Ås, Norway
2
Faculty of Engineering, Østfold University College, 1671 Kråkerøy, Norway
3
Department of Geography, Norwegian University of Science and Technology, 7491 Trondheim, Norway
4
University College of Southeast Norway, 3603 Kongsberg, Norway
*
Author to whom correspondence should be addressed.
Academic Editor: Marco Franchini
Received: 9 March 2017 / Revised: 11 April 2017 / Accepted: 25 April 2017 / Published: 1 May 2017
View Full-Text   |   Download PDF [1184 KB, uploaded 5 May 2017]   |  

Abstract

Urban flooding caused by heavy rainfall is expected to increase in the future. The main purpose of this study was to investigate the variables characterizing the placement of a house, which seem to have an impact when it comes to the exposure to floods. From the same region in Norway, data from 347 addresses were derived. All addresses were either associated with insurance claims caused by flooding or were randomly selected. A multivariate statistical model, Partial Least Square Regression (PLS), was used. Among others, the analysis has shown that the upstream, sealed area is the most significant variable for characterizing properties’ exposure to urban flooding. The model confirms that flooding tends to occur near old combined sewer mains and in concave curvature, and houses located in steep slopes seem to be less exposed. Using this method, it is possible to rank and quantify significant exposure variables contributing to urban floods within a region. Results from the PLS-analysis might provide important input to professionals, when planning and prioritizing measures. It can also predict flood-prone areas and make residents aware of the risks, which may induce them to implement preventive measures. View Full-Text
Keywords: urban flooding; exposure to floods; insurance claims; partial least square regression urban flooding; exposure to floods; insurance claims; partial least square regression
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Torgersen, G.; Rød, J.K.; Kvaal, K.; Bjerkholt, J.T.; Lindholm, O.G. Evaluating Flood Exposure for Properties in Urban Areas Using a Multivariate Modelling Technique. Water 2017, 9, 318.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Water EISSN 2073-4441 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top