@article {bnh-5020,
	title = {Predictive applications of Australian flood loss models after a temporal and spatial transfer},
	journal = {Geomatics, Natural Hazards and Risk },
	volume = {9},
	year = {2018},
	month = {03/2018},
	pages = {14},
	chapter = {416},
	abstract = {<p>In recent decades, considerably greater flood losses have increased attention to flood risk evaluation. This study used data-sets collected from Queensland flood events and investigated the predictive capacity of three new Australian flood loss models to assess the extent of physical damages, after a temporal and spatial transfer. The models{\textquoteright} predictive power is tested for precision, variation, and reliability. The performance of a new Australian flood loss function was contrasted with two tree-based damage models, one pruned and one un-pruned. The tree-based models are grown based on the interaction of flood loss ratio with 13 examined predictors gathered from flood specifications, building characteristics, and mitigation actions. Besides an overall comparison, the prediction capacity is also checked for some sub-classes of water depth and some groups of building-type.</p><p>It has been shown that considering more details of the flood damage process can improve the predictive capacity of damage prediction models. In this regard, complexity with parameters with low predictive power may lead to more uncertain results. On the other hand, it has also been demonstrated that the probability analysis approach can make damage models more reliable when they are subjected to use in different flooding events.</p>},
	keywords = {damage assessment, Disaster risk reduction, flood risk, Natural hazards, predictive capacity},
	url = {https://www.tandfonline.com/doi/full/10.1080/19475705.2018.1445666},
	author = {Roozbeh Hasanzadeh Nafari and Tuan Ngo}
}
