A glimpse into the future of exposure and vulnerabilities in cities? Modelling of residential location choice of urban population with random forest

Abstract

<jats:p>Abstract. Disaster risk is conceived as the interaction of hazard, exposure, and vulnerability. Especially in urban environments, exposure and vulnerability are highly dynamic risk components, both being shaped by a complex and continuous reorganization and redistribution of assets within the urban space, including the residence of urban dwellers. This case study for the city of Leipzig, Germany, proposes an indirect, machine learning-based approach for the prediction of residential choice behaviour to explore how exposure and vulnerabilities are shaped by the residential location choice process. The proposed approach reveals hot spots and cold spots of residential choice for distinct socioeconomic groups exhibiting heterogeneous preferences. We discuss the relationship between observed patterns and disaster risk through the lens of exposure and vulnerability, as well as links to urban planning. Avenues for future research include the operational strengthening of these linkages for more effective disaster risk management. </jats:p>

Probabilistic Water Demand Forecasting Using Quantile Regression Algorithms

“…Machine and statistical learning algorithms (see, e.g., Alpaydin, 2014; Hastie et al., 2009; James et al., 2013; Witten et al., 2017) can be reliably automated and applied at scale (Papacharalampous et al., 2019). Therefore, they are befitting and increasingly adopted for solving urban water demand forecasting problems (see, e.g., Duerr et al., 2018; Herrera et al., 2010; Herrera et al., 2011; Lee & Derrible, 2020; Nunes Carvalho et al., 2021; Quilty & Adamowski, 2018; Quilty et al., 2016; Smolak et al., 2020; Xenochristou & Kapelan, 2020; Xenochristou et al., 2020; Xenochristou et al., 2021), and several other water informatics problems (see, e.g., Althoff, Dias, et al., 2020; Althoff, Filgueiras, & Rodrigues, 2020; Althoff, Bazame, & Garcia, 2021; Markonis & Strnad, 2020; Rahman, Hosono, Kisi, et al., 2020; Rahman, Hosono, Quilty, et al., 2020; Sahoo et al., 2019; Scheuer et al., 2021; Tyralis, Papacharalampous, & Langousis, 2021; Tyralis & Papacharalampous, 2017; Xu, Chen, Zhang, & Chen, 2020; Xu, Chen, Moradkhani, et al., 2020).…”