Tree‐based flood damage modeling of companies: Damage processes and model performance

Sieg, Tobias; Vogel, Kristin; Merz, Bruno; Kreibich, Heidi

doi:10.1002/2017wr020784

Cited by 43 publications

(96 citation statements)

References 57 publications

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“…The random forest, which is an ensemble learning method of n tree-based classifiers, is an alternative method that was used in a study case in Germany to identify variables that influence damages and model flood damages [49]. The AHP method presents many advantages: it is appropriate for group decision making; it works with both quantitative and qualitative input data; the stakeholders can answer the pairwise question easily; it allows a hierarchical configuration of the criteria; it enables evaluating the consistency of preference; and it provides a measure of a level of consistent judgment supported by a theoretical background.…”

Section: Intensity Of Importance Definitionmentioning

confidence: 99%

Flood Risk Mapping Using GIS and Multi-Criteria Analysis: A Greater Toronto Area Case Study

2018

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Given the increase in flood events in recent years, accurate flood risk assessment is an important component of flood mitigation in urban areas. This research aims to develop updated and accurate flood risk maps in the Don River Watershed within the Great Toronto Area (GTA). The risk maps use geographical information systems (GIS) and multi-criteria analysis along with the application of Analytical Hierarchy Process methods to define and quantify the optimal selection of weights for the criteria that contribute to flood risk. The flood hazard maps were generated for four scenarios, each with different criteria (S1, S2, S3, and S4). The base case scenario (S1) is the most accurate, since it takes into account the floodplain map developed by the Toronto and Region Conservation Authority. It also considers distance to streams (DS), height above nearest drainage (HAND), slope (S), and the Curve Number (CN). S2 only considers DS, HAND, and CN, whereas S3 considers effective precipitation (EP), DS, HAND, and S. Lastly, S4 considers total precipitation (TP), DS, HAND, S, and CN. In addition to the flood hazard, the social and economic vulnerability was included to determine the total flood vulnerability in the watershed under three scenarios; the first one giving a higher importance to the social vulnerability, the second one giving equal importance to both social and economic vulnerability, and the third one giving more importance to the economic vulnerability. The results for each of the four flood scenarios show that the flood risk generated for S2 is the most similar to the base case (S1), followed by S3 and S4. The inclusion of social and economic vulnerability highlights the impacts of floods that are typically ignored in practice. It will allow watershed managers to make more informed decisions for flood mitigation and protection. The most important outcome of this research is that by only using the digital elevation model, the census data, the streams, land use, and soil type layers, it is possible to obtain a reliable flood risk map (S2) using a simplified method as compared to more complex flood risk methods that use hydraulic and hydrological models to generate flood hazard maps (as was the case for S1).

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Section: Intensity Of Importance Definitionmentioning

confidence: 99%

Flood Risk Mapping Using GIS and Multi-Criteria Analysis: A Greater Toronto Area Case Study

2018

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“…Publicly available data sets, such as from openstreetmap.org, can provide specific information such as the occupancy or height of buildings at the object level (Figueiredo & Martina, 2016). This kind of information could be used to assess damage caused to individual buildings by means of, for example, engineering-based hurricane damage models (Pita et al, 2013;Vickery et al, 2006) or multivariable flood damage models (Merz et al, 2013;Sieg et al, 2017;Wagenaar et al, 2017). So far, state-of-the-art damage models (Prahl et al, 2016;Sieg et al, 2017;Wagenaar et al, 2017) are not able to keep up with this development causing a gap between what is currently done and what improvements would theoretically be feasible.…”

Section: 1029/2018ef001122mentioning

confidence: 99%

“…Considering only one object, it can be seen that the point estimate, that is, the mean, is a rather unlikely value within the skewed damage distribution (Figure 2, k = 1, and Figure S1). In most cases, the point estimate for single objects will overestimate or underestimate the flood damage, which results in large errors as observed in Seifert et al (2010), Sieg et al (2017), and Wagenaar et al (2017). The higher the number of objects, by summing up their damage estimates, the better the empirical distribution fits to a normal distribution (Central Limit Theorem).…”

Section: The Role Of Vulnerability Uncertaintymentioning

confidence: 99%

“…Despite the progress in damage estimation in recent years (Figueiredo et al, 2018;Kreibich et al, 2016;Schröter et al, 2014;Sieg et al, 2017;Wagenaar et al, 2017), the available damage models are not able to describe the damage process reliably, and the process is stochastic to a substantial extent. Therefore, as implemented in this approach, the distribution of the damage estimate should be assessed at every spatial scale in order to report the lack of knowledge about the damage estimates.…”

Section: The Role Of Vulnerability Uncertaintymentioning

confidence: 99%

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Seamless Estimation of Hydrometeorological Risk Across Spatial Scales

et al. 2019

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Hydrometeorological hazards caused losses of approximately 110 billion U.S. Dollars in 2016 worldwide. Current damage estimations do not consider the uncertainties in a comprehensive way, and they are not consistent between spatial scales. Aggregated land use data are used at larger spatial scales, although detailed exposure data at the object level, such as openstreetmap.org, is becoming increasingly available across the globe. We present a probabilistic approach for object‐based damage estimation which represents uncertainties and is fully scalable in space. The approach is applied and validated to company damage from the flood of 2013 in Germany. Damage estimates are more accurate compared to damage models using land use data, and the estimation works reliably at all spatial scales. Therefore, it can as well be used for pre‐event analysis and risk assessments. This method takes hydrometeorological damage estimation and risk assessments to the next level, making damage estimates and their uncertainties fully scalable in space, from object to country level, and enabling the exploitation of new exposure data.

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“…Per analysis, cases with missing values were excluded. Further details on the surveys and the data processing are published by Thieken et al (2007Thieken et al ( , 2016aThieken et al ( , 2017, Kreibich et al (2007b) and Sieg et al (2017). Answers provided to questions mainly related to flood warning and emergency measures are analysed for this study.…”

Section: Surveys and Datamentioning

confidence: 99%

New insights into flood warning reception and emergency response by affected parties

Kreibich

Müller²,

Schröter

et al. 2017

Nat. Hazards Earth Syst. Sci.

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Abstract. Flood damage can be mitigated if the parties at risk are reached by flood warnings and if they know how to react appropriately. To gain more knowledge about warning reception and emergency response of private households and companies, surveys were undertaken after the August 2002 and the June 2013 floods in Germany. Despite pronounced regional differences, the results show a clear overall picture: in 2002, early warnings did not work well; e.g. many households (27 %) and companies (45 %) stated that they had not received any flood warnings. Additionally, the preparedness of private households and companies was low in 2002, mainly due to a lack of flood experience. After the 2002 flood, many initiatives were launched and investments undertaken to improve flood risk management, including early warnings and an emergency response in Germany. In 2013, only a small share of the affected households (5 %) and companies (3 %) were not reached by any warnings. Additionally, private households and companies were better prepared. For instance, the share of companies which have an emergency plan in place has increased from 10 % in 2002 to 34 % in 2013. However, there is still room for improvement, which needs to be triggered mainly by effective risk and emergency communication. The challenge is to continuously maintain and advance an integrated early warning and emergency response system even without the occurrence of extreme floods.

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Tree‐based flood damage modeling of companies: Damage processes and model performance

Cited by 43 publications

References 57 publications

Flood Risk Mapping Using GIS and Multi-Criteria Analysis: A Greater Toronto Area Case Study

Flood Risk Mapping Using GIS and Multi-Criteria Analysis: A Greater Toronto Area Case Study

Seamless Estimation of Hydrometeorological Risk Across Spatial Scales

New insights into flood warning reception and emergency response by affected parties

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