Data-driven Strategies for Affordable Housing: A Hybrid Genetic Algorithm-Machine Learning Optimization Model in the Melbourne Metropolitan Area

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.

In this paper, a newly derived flood loss function for Australian residential structures (FLFA_rs) is presented and calibrated by using historic data collected from an extreme event in Queensland, Australia, that occurred in 2013. Afterwards, the performance of FLFA_rs 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.