The use of insurance data in the analysis of Surface Water Flood events – A systematic review

Gradeci, Klodian; Labonnote, Nathalie; Sivertsen, Edvard; Time, Berit

doi:10.1016/j.jhydrol.2018.10.060

Cited by 29 publications

(20 citation statements)

References 57 publications

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“…The main barrier of this approach is the availability of insurance data. These data are mostly restricted due to data privacy legislation, reputational issues, business secrets, loyalty to customers, or issues of competition for market share [29]. By presenting the benefits of these data, we want to contribute to the discussion on opening damage data archives for research or for establishing national damage databases.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, additional explaining variables have to be considered in future studies as well, e.g. damage variables including the characteristics of the buildings affected by SWFs such as physical vulnerability, geographic variables including the characteristics of the sewer system, terrain parameters around the buildings, and possibly also demographic variables [29]. These variables are even more relevant when not only the occurrence of damage-relevant SWFs are predicted, but also the actual amount of loss.…”

Section: Discussionmentioning

confidence: 99%

“…Moncoulon et al [28] analyzed insurance market exposure to both FFs and SWFs. A review of using damage data and insurance data in the analysis of SWFs is given by Gradeci et al [29]. Insurance claim data are also used for validating flood models [30][31][32].…”

Section: Introductionmentioning

confidence: 99%

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Characterizing precipitation events leading to surface water flood damage over large regions of complex terrain

Bernet

Trefalt

Martius

et al. 2019

Environ. Res. Lett.

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Surface water floods (SWFs) that lead to household losses are mainly localized phenomena. Research on describing the associated precipitation characteristics has previously been based on case studies and on the derivation of local rainfall thresholds, but no approaches have yet been presented on the national scale. Here, we propose a new way to overcome this scaling problem. We linked a gridded precipitation dataset based on both rainfall gauges and radar data with geolocated insurance claims for all of Switzerland. We show that the absolute thresholds vary markedly over complex terrain, and we thus propose basing early warning systems for predicting damage-relevant SWF events on local quantiles of maximum intensity and the total sum of event precipitation. A threshold model based on these two parameters is able to classify rainfall events potentially leading to damage-relevant SWF events over large areas of complex terrain, including high mountains and lowland areas, and a variety of geological conditions. Our approach is an important step towards the development of impact-based early warning systems. Weather warning agencies or insurance companies can build upon these findings to find workarounds for issuing user-targeted warnings at national scale or for nowcasting purposes.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Characterizing precipitation events leading to surface water flood damage over large regions of complex terrain

Bernet

Trefalt

Martius

et al. 2019

Environ. Res. Lett.

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“…The preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology was implemented after the initial literature search without any specific filters for language or publication type. In general, the PRISMA framework consists of six steps [28], and each of them provides specific criteria for filtering the collected database. The first exclusion criterion is based on extraction of the literature by type of publication and language preferences.…”

Section: Methodsmentioning

confidence: 99%

A Systematic Literature Review of Non-Invasive Indoor Thermal Discomfort Detection

Marchenko¹,

Salaj²

2020

Applied Sciences

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Since 1997, scientists have been trying to utilize new non-invasive approaches for thermal discomfort detection, which promise to be more effective for comparing frameworks that need direct responses from users. Due to rapid technological development in the bio-metrical field, a systematic literature review to investigate the possibility of thermal discomfort detection at the work place by non-invasive means using bio-sensing technology was performed. Firstly, the problem intervention comparison outcome context (PICOC) framework was introduced in the study to identify the main points for meta-analysis and, in turn, to provide relevant keywords for the literature search. In total, 2776 studies were found and processed using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology. After filtering by defined criterion, 35 articles were obtained for detailed investigation with respect to facility types used in the experiment, amount of people for data collection and algorithms used for prediction of the thermal discomfort event. The given study concludes that there is potential for the creation of non-invasive thermal discomfort detection models via utilization of bio-sensing technologies, which will provide a better user interaction with the built environment, potentially decrease energy use and enable better productivity. There is definitely room for improvement within the field of non-invasive thermal discomfort detection, especially with respect to data collection, algorithm implementation and sample size, in order to have opportunities for the deployment of developed solutions in real life. Based on the literature review, the potential of novel technology is seen to utilize a more intelligent approach for performing non-invasive thermal discomfort prediction. The architecture of deep neural networks should be studied more due to the specifics of its hidden layers and its ability of hierarchical data extraction. This machine learning algorithm can provide a better model for thermal discomfort detection based on a data set with different types of bio-metrical variables.

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“…This method can help scholars conduct a clearer and more valid review. The following is a detailed description of how to use this method for a systematic overview (Gradeci et al 2019).…”

Section: Methodsmentioning

confidence: 99%

Urban surface water flood modelling – a comprehensive review of current models and future challenges

Ke¹,

Guan²,

Yu³

2020

Preprint

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Abstract. Urbanisation is an irreversible trend as a result of social and economic development. Urban areas, with high concentration of population, key infrastructure, and businesses are extremely vulnerable to flooding and may suffer severe socio-economic losses due to climate change. Urban flood modelling tools are in demand to predict surface water inundation caused by intense rainfall and to manage associated flood risks in urban areas. These tools have been rapidly developing in recent decades. In this study, we present a comprehensive review of the advanced urban flood models and emerging approaches for predicting urban surface water flooding driven by intense rainfall. The study explores the advantages and limitations of existing model types, highlights the most recent advances and identifies major challenges. Issues of model complexities, scale effects, and computational efficiency are also analysed. The results will inform scientists, engineers, and decision-makers of the latest developments and guide the model selection based on desired objectives.

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The use of insurance data in the analysis of Surface Water Flood events – A systematic review

Cited by 29 publications

References 57 publications

Characterizing precipitation events leading to surface water flood damage over large regions of complex terrain

Characterizing precipitation events leading to surface water flood damage over large regions of complex terrain

A Systematic Literature Review of Non-Invasive Indoor Thermal Discomfort Detection

Urban surface water flood modelling – a comprehensive review of current models and future challenges

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