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

Daniel Jato-Espino, Nora Sillanpää, Ignacio Andrés-Doménech, Jorge Rodriguez-Hernandez

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


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.
Original languageEnglish
Title of host publicationNew Trends in Urban Drainage Modelling
Subtitle of host publicationUDM 2018
EditorsGiorgio Mannina
ISBN (Electronic)978-3-319-99867-1
ISBN (Print)978-3-319-99866-4
Publication statusPublished - 2019
MoE publication typeA4 Conference publication
EventInternational Conference on Urban Drainage Modelling - Palermo, Italy
Duration: 23 Sept 201826 Sept 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


  • Flood hazard modelling
  • Multiple regression analysis
  • Sewersheds


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