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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume X-4-2024
https://doi.org/10.5194/isprs-annals-X-4-2024-175-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/isprs-annals-X-4-2024-175-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
18 Oct 2024
| 18 Oct 2024
Data-driven Strategies for Affordable Housing: A Hybrid Genetic Algorithm-Machine Learning Optimization Model in the Melbourne Metropolitan Area
Peyman Jafary
Building 4.0 CRC, Caulfield East, Victoria, Australia
Centre for Spatial Data Infrastructures and Land Administration, Department of Infrastructure Engineering, The University of Melbourne, Melbourne, Victoria, Australia
Davood Shojaei
Centre for Spatial Data Infrastructures and Land Administration, Department of Infrastructure Engineering, The University of Melbourne, Melbourne, Victoria, Australia
Sara Pishgahi
Centre for Urban Research, School of Global, Urban and Social Studies, RMIT University, Melbourne, Victoria, Australia
Abbas Rajabifard
Centre for Spatial Data Infrastructures and Land Administration, Department of Infrastructure Engineering, The University of Melbourne, Melbourne, Victoria, Australia
Tuan Ngo
Building 4.0 CRC, Caulfield East, Victoria, Australia
Department of Infrastructure Engineering, The University of Melbourne, Melbourne, Victoria, Australia
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Short summary
Short summary
Floods are frequent natural hazards in Italy, triggering significant adverse consequences on the economy every year. Their impact is expected to worsen in the near future due to socio-economic development and climate variability. To be able to reduce the probability and magnitude of expected economic losses, flood risk managers need to be correctly informed about the potential damage from flood hazards. In this study, we have developed a new and accurate model for Italian residential buildings.
R. Hasanzadeh Nafari, T. Ngo, and W. Lehman
Nat. Hazards Earth Syst. Sci., 16, 15–27, https://doi.org/10.5194/nhess-16-15-2016, https://doi.org/10.5194/nhess-16-15-2016, 2016
Short summary
Short summary
In this paper, a newly derived flood loss function for Australian residential structures (FLFArs) is presented and calibrated by using historic data collected from an extreme event in Queensland, Australia, that occurred in 2013. Afterwards, the performance of FLFArs has been compared with the observed damage data collected from a 2012 flood event in Maranoa, Queensland. Based on this analysis, validation of FLFArs has been performed in terms of Australian geographical conditions.
The International Society for Photogrammetry and Remote Sensing is a non-governmental organization devoted to the development of international cooperation for the advancement of photogrammetry and remote sensing and their applications. The Society operates without any discrimination on grounds of race, religion, nationality, or political philosophy.
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