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Studies have applied a range of theoretical models, including Protection Motivation Theory (PMT) (Poussin et al., 2014; Haer et al., 2016; Bamberg et al., 2017; Erdlenbruch and Bonte, 2018; Mertens et al., 2018; Botzen et al., 2019), vested interest theory (De Dominicis et al., 2014), the Theory of Planned Behavior (Nox and Myles, 2017), the Transtheoretical Model (Martin et al., 2007), and approaches based on the findings of behavioral economics (Linnemayr et al., 2016; Mol et al., 2020), as well as hybrids of these models (Martin et al., 2007). Generally, perception and behavior research seeks to explore and help explain perceptions, behavior and behavioral intentions related to disaster risk reduction planning by individuals, and to offer recommendations on disaster risk reduction initiatives that include motivating individual property owners to undertake household or property-level risk reduction actions. Behavioral economics approaches have been advanced to motivate behavior with respect to disaster risk reduction (Mol et al., 2020). While these approaches have to date rarely been applied in the field of disaster preparedness and response, they have resulted in favorable results in similar decision making contexts (where benefits of investments are uncertain, where behavior change may benefit others more than the individual making the change).

112. Xia, X.; Liang, Q.; Ming, X. A full-scale fluvial flood modelling framework based on a high-performance integrated hydrodynamic modelling system (HiPIMS). 113. Sanders, B.F.; Schubert, J.E.; Detwiler, R.L. ParBreZo: A parallel, unstructured grid, Godunov-type, shallow-water code for high-resolution flood inundation modeling at the regional scale. 114. Costabile, P.; Costanzo, C. A 2D-SWEs framework for efficient catchment-scale simulations: Hydrodynamic scaling properties of river networks and implications for non-uniform grids generation. 115. Padulano, R.; Costabile, P.; Costanzo, C.; Rianna, G.; Del Giudice, G.; Mercogliano, P. Using the present to estimate the future: A simplified approach for the quantification of climate change effects on urban flooding by scenario analysis. Hydrol. Process. 2021, 35, e14436. 116. Bermúdez, M.; Cea, L.; Puertas, J. A rapid flood inundation model for hazard mapping based on least squares support vector machine regression. J. Flood Risk Manag. 117. Bomers, A. Predicting outflow hydrographs of potential dike breaches in a bifurcating river system using narx neural networks. 118. Chu, H.; Wu, W.; Wang, Q.J.; Nathan, R.; Wei, J. An ANN-based emulation modelling framework for flood inundation modelling: Application, challenges and future directions.