Multiple Regression Analysis as a Comprehensive Tool to Model Flood Hazard in Sewersheds

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Original languageEnglish
Title of host publicationNew Trends in Urban Drainage Modelling
Subtitle of host publicationUDM 2018
EditorsGiorgio Mannina
Publication statusPublished - 2019
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Urban Drainage Modelling - Palermo, Italy
Duration: 23 Sep 201826 Sep 2018
Conference number: 11

Publication series

NameGreen Energy and Technology
PublisherSpringer International Publishing
ISSN (Print)1865-3529
ISSN (Electronic)1865-3537


ConferenceInternational Conference on Urban Drainage Modelling
Abbreviated titleUDM


  • Daniel Jato-Espino
  • Nora Sillanpää

  • Ignacio Andrés-Doménech
  • Jorge Rodriguez-Hernandez

Research units

  • Universidad de Cantabria
  • Polytechnic University of Valencia


Flood modelling in urban areas is usually undertaken using stormwater tools, which are complex and time-consuming in terms of parametrization. To replace them, this research developed a methodology for predicting flooding probability in urban watersheds (sewersheds) through the modelling of peak flow rates from a set of watershed and sewer network-related factors relevant for the occurrence of floods. This was addressed through the stepped integration of Multiple Linear Regression (MLR), Multiple Nonlinear Regression (MNR) and Multiple Binary Logistic Regression (MBLR). A case study of a sewershed in Espoo (Finland) was used to validate the proposed approach and test it for future estimates, enabling the prediction of flooding probabilities under different Climate Change scenarios.

    Research areas

  • Flood hazard modelling, Multiple regression analysis, Sewersheds

ID: 27791591