Preprints
https://doi.org/10.5194/nhess-2023-113
https://doi.org/10.5194/nhess-2023-113
25 Aug 2023
 | 25 Aug 2023
Status: this preprint was under review for the journal NHESS but the revision was not accepted.

Application of machine learning for integrated flood risk assessment: Case study of Hurricane Harvey in Houston, Texas

Behrang Bidadian, Aaron E. Maxwell, and Michael P. Strager

Abstract. Flood risk, encompassing hazard, exposure, and vulnerability is defined concerning potential losses. Machine learning techniques have gained traction among researchers to address the complexities of multi-variable flood risk assessment models and overcome issues associated with non-linear relationships. However, the focus has primarily been on flood hazard prediction rather than comprehensive risk assessment and damage estimations. Therefore, there is a need for experiments that combine risk elements using such methods. To address this need, this study utilized the Random Forest algorithm to analyze the correlations between the physical flood damage caused by Hurricane Harvey in 2017 in Houston, Texas and certain hazard, exposure, and vulnerability-related variables. The study identified poorly drained soils as the primary contributor to the losses, followed by population density and the ratio of developed lands with medium intensity. The study's findings also explored the reasons for the unexpectedly low importance of social vulnerability factors compared to the environmental justice concept. These findings and conclusions can provide insights to planners and stakeholders enhancing their understanding of the underlying causes contributing to flood risk. Future research can expand upon this study's methodology and findings by incorporating additional factors related to climate change.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Behrang Bidadian, Aaron E. Maxwell, and Michael P. Strager

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2023-113', Anonymous Referee #1, 19 Sep 2023
    • AC1: 'Reply on RC1', Behrang Bidadian, 28 Sep 2023
  • RC2: 'Comment on nhess-2023-113', Anonymous Referee #2, 12 Dec 2023
    • AC2: 'Reply on RC2', Behrang Bidadian, 27 Dec 2023

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2023-113', Anonymous Referee #1, 19 Sep 2023
    • AC1: 'Reply on RC1', Behrang Bidadian, 28 Sep 2023
  • RC2: 'Comment on nhess-2023-113', Anonymous Referee #2, 12 Dec 2023
    • AC2: 'Reply on RC2', Behrang Bidadian, 27 Dec 2023
Behrang Bidadian, Aaron E. Maxwell, and Michael P. Strager
Behrang Bidadian, Aaron E. Maxwell, and Michael P. Strager

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Short summary
Machine learning (ML) techniques offer a comprehensive approach to flood risk assessment and damage estimation. This study used ML to analyze flood damage caused by Hurricane Harvey in Houston, Texas. It identified poorly drained soils, population density, and medium-intensity developed lands as the main contributors to the losses. The findings can help planners and stakeholders understand the causes of flood risk. Future research can build on this study by considering climate change factors.
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