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

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

doi:10.1007/978-3-319-99867-1_98

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 proceeding › Conference article in proceedings › Scientific › peer-review

Abstract

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 language	English
Title of host publication	New Trends in Urban Drainage Modelling
Subtitle of host publication	UDM 2018
Editors	Giorgio Mannina
Publisher	Springer
Pages	571-575
ISBN (Electronic)	978-3-319-99867-1
ISBN (Print)	978-3-319-99866-4
DOIs	https://doi.org/10.1007/978-3-319-99867-1_98
Publication status	Published - 2019
MoE publication type	A4 Conference publication
Event	International Conference on Urban Drainage Modelling - Palermo, Italy Duration: 23 Sept 2018 → 26 Sept 2018 Conference number: 11

Publication series

Name	Green Energy and Technology
Publisher	Springer International Publishing
ISSN (Print)	1865-3529
ISSN (Electronic)	1865-3537

Conference

Conference	International Conference on Urban Drainage Modelling
Abbreviated title	UDM
Country/Territory	Italy
City	Palermo
Period	23/09/2018 → 26/09/2018

Keywords

Flood hazard modelling
Multiple regression analysis
Sewersheds

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-319-99867-1_98

Cite this

@inproceedings{42ad20c65aea4aa49f2f607aef2d2d93,

title = "Multiple Regression Analysis as a Comprehensive Tool to Model Flood Hazard in Sewersheds",

abstract = "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.",

keywords = "Flood hazard modelling, Multiple regression analysis, Sewersheds",

author = "Daniel Jato-Espino and Nora Sillanp{\"a}{\"a} and Ignacio Andr{\'e}s-Dom{\'e}nech and Jorge Rodriguez-Hernandez",

year = "2019",

doi = "10.1007/978-3-319-99867-1_98",

language = "English",

isbn = "978-3-319-99866-4",

series = "Green Energy and Technology",

publisher = "Springer",

pages = "571--575",

editor = "Giorgio Mannina",

booktitle = "New Trends in Urban Drainage Modelling",

address = "Germany",

note = "International Conference on Urban Drainage Modelling, UDM ; Conference date: 23-09-2018 Through 26-09-2018",

}

Jato-Espino, D, Sillanpää, N, Andrés-Doménech, I & Rodriguez-Hernandez, J 2019, Multiple Regression Analysis as a Comprehensive Tool to Model Flood Hazard in Sewersheds. in G Mannina (ed.), New Trends in Urban Drainage Modelling: UDM 2018. Green Energy and Technology, Springer, pp. 571-575, International Conference on Urban Drainage Modelling, Palermo, Italy, 23/09/2018. https://doi.org/10.1007/978-3-319-99867-1_98

Multiple Regression Analysis as a Comprehensive Tool to Model Flood Hazard in Sewersheds. / Jato-Espino, Daniel; Sillanpää, Nora; Andrés-Doménech, Ignacio et al.
New Trends in Urban Drainage Modelling: UDM 2018. ed. / Giorgio Mannina. Springer, 2019. p. 571-575 (Green Energy and Technology).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

TY - GEN

T1 - Multiple Regression Analysis as a Comprehensive Tool to Model Flood Hazard in Sewersheds

AU - Jato-Espino, Daniel

AU - Sillanpää, Nora

AU - Andrés-Doménech, Ignacio

AU - Rodriguez-Hernandez, Jorge

N1 - Conference code: 11

PY - 2019

Y1 - 2019

N2 - 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.

AB - 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.

KW - Flood hazard modelling

KW - Multiple regression analysis

KW - Sewersheds

U2 - 10.1007/978-3-319-99867-1_98

DO - 10.1007/978-3-319-99867-1_98

M3 - Conference article in proceedings

SN - 978-3-319-99866-4

T3 - Green Energy and Technology

SP - 571

EP - 575

BT - New Trends in Urban Drainage Modelling

A2 - Mannina, Giorgio

PB - Springer

T2 - International Conference on Urban Drainage Modelling

Y2 - 23 September 2018 through 26 September 2018

ER -

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

Abstract

Publication series

Conference

Keywords

UN SDGs

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Cite this