Applying Machine Learning Techniques to Identify Key Factors Motivating Flood-Prone Residents to Implement Private Flood Mitigation Measures-Reference-Cited by-同舟云学术

Applying Machine Learning Techniques to Identify Key Factors Motivating Flood-Prone Residents to Implement Private Flood Mitigation Measures

Published:2024-08 Issue:3 Volume:25 Page:
ISSN:1527-6988
Container-title:Natural Hazards Review
language:en
Short-container-title:Nat. Hazards Rev.

Author:

Fouladi Semnan Afshin¹^ORCID,Maqsood Tariq²,Venkatesan Srikanth³^ORCID

Affiliation:

1. Sessional Lecturer, Civil and Infrastructure Engineering Discipline, STEM College, RMIT Univ., Melbourne, VIC 3000, Australia (corresponding author). ORCID: .

2. Associate Professor, Civil and Infrastructure Engineering Discipline, STEM College, RMIT Univ., Melbourne, VIC 3000, Australia.

3. Senior Lecturer, Civil and Infrastructure Engineering Discipline, STEM College, RMIT Univ., Melbourne, VIC 3000, Australia. ORCID: .

Publisher

American Society of Civil Engineers (ASCE)

Link

https://ascelibrary.org/doi/pdf/10.1061/NHREFO.NHENG-1928

Reference85 articles.

1. Agresti, A. 2018. An introduction to categorical data analysis. Hoboken, NJ: Wiley.

2. AIDR (Australian Institute for Disaster Resilience). 2017. “Managing the floodplain: A guide to best practice in flood risk management in Australia.” In Australian disaster resilience handbook 7. 3rd ed. East Melbourne, VIC, Australia: AIDR.

3. Unpacking Protection Motivation Theory: evidence for a separate protective and non-protective route in private flood mitigation behavior

4. Threat, coping and flood prevention – A meta-analysis

5. Bonafilia D. B. Tellman T. Anderson and E. Issenberg. 2020. “Sen1Floods11: A georeferenced dataset to train and test deep learning flood algorithms for sentinel-1.” In Proc. IEEE/CVF Conf. on Computer Vision and Pattern Recognition Workshops. New York: Computer Vision Foundation.