Flood risk modelling aims to quantify the probability of flooding and the resulting consequences for exposed elements. The assessment of flood damage is a core task that requires the description of complex flood damage processes including the influences of flooding intensity and vulnerability characteristics. Multi-variable modelling approaches are better suited for this purpose than simple stage–damage functions. However, multi-variable flood vulnerability models require detailed input data and often have problems in predicting damage for regions other than those for which they have been developed. A transfer of vulnerability models usually results in a drop of model predictive performance. Here we investigate the questions as to whether data from the open-data source OpenStreetMap is suitable to model flood vulnerability of residential buildings and whether the underlying standardized data model is helpful for transferring models across regions. We develop a new data set by calculating numerical spatial measures for residential-building footprints and combining these variables with an empirical data set of observed flood damage. From this data set random forest regression models are learned using regional subsets and are tested for predicting flood damage in other regions. This regional split-sample validation approach reveals that the predictive performance of models based on OpenStreetMap building geometry data is comparable to alternative multi-variable models, which use comprehensive and detailed information about preparedness, socio-economic status and other aspects of residential-building vulnerability. The transfer of these models for application in other regions should include a test of model performance using independent local flood data. Including numerical spatial measures based on OpenStreetMap building footprints reduces model prediction errors (MAE – mean absolute error – by 20 % and MSE – mean squared error – by 25 %) and increases the reliability of model predictions by a factor of 1.4 in terms of the hit rate when compared to a model that uses only water depth as a predictor. This applies also when the models are transferred to other regions which have not been used for model learning. Further, our results show that using numerical spatial measures derived from OpenStreetMap building footprints does not resolve all problems of model transfer. Still, we conclude that these variables are useful proxies for flood vulnerability modelling because these data are consistent (i.e. input variables and underlying data model have the same definition, format, units, etc.) and openly accessible and thus make it easier and more cost-effective to transfer vulnerability models to other regions.

Floods have huge socio-economic impacts globally.
Driven by increasing exposure, as well as
increasing frequency and intensity of extreme weather events,
consequences of flooding have sharply risen during recent decades

Both synthetic (e.g.

We test the hypothesis that numerical spatial measures derived from OSM building footprints provide useful information for the estimation of flood losses to residential buildings. From the underlying consistent OSM data model and standardized calculation of spatial measures, we expect an improvement of the spatial transfer of flood vulnerability models across regions. Accordingly, the research objectives are (i) to understand which building-geometry-related variables are useful to describe building vulnerability, (ii) to learn predictive flood vulnerability models, and (iii) to test and evaluate model transfer across regions. In Sect. 2 the data sources, the derived variables and the preparation of data sets are described. Section 3 introduces the methods to identify predictor variables and to derive predictive models. Further, it describes the set-up for testing and evaluating model performance in spatial transfers. The results from these analyses are reported and discussed in Sect. 4. Conclusions are drawn in Sect. 5.

We use an empirical data set of relative loss to residential buildings and
influencing factors which has been collected via computer-aided telephone
interview (CATI) data during survey campaigns after major floods in Germany since
2002. Another data source is OSM

CATI surveys were conducted with affected
private households ex post major floods in Germany. The regional focal points
of flood impacts were the Elbe catchment in eastern Germany and the Danube
catchment in southern Germany. Particularly noteworthy are the floods of 2002
and 2013, which caused economic losses of EUR 11.6 billion (reference year 2005) and
EUR 8 billion respectively in Germany

Preselected variables from CATI surveys; C: continuous, O: ordinal, N: nominal-scaled variables.

OSM is a free web-based map service built on
the activity of registered users who contribute to the database by adding,
editing or deleting features based on their local knowledge. The contributors
use GPS devices and satellite as well as aerial imagery to verify the accuracy
of the map. OSM is an open-data project, and the cartographic information can be
downloaded, altered and redistributed under the Open Data Commons Open Database
License (ODbL)

The OSM and CATI data sets have been conflated in order to link the empirically
observed variables rloss and wst with OSM data for individual residential
buildings. This operation uses the geolocation information of both data
sources. The CATI data are provided with address details including community,
postal code, street name and the house number ranges in blocks of five numbers.
Geocoding algorithms including open web API (application programming interface)
services like Google (

OSM is a spatial data set including georeferenced building outlines. The
geolocated interviews are spatially matched with OSM building polygons using an
overlay operation which merges interview points with OSM building polygons. In
view of limited address details regarding the building house number ranges and
inherent inaccuracies of geocoding databases and algorithms

Regional subdivision of the data set for spatial split-sample testing (Dresden municipality, the Elbe catchment and the Danube catchment).

Information about the building geometry is useful to support the estimation of
flood losses to residential buildings

Variables of the amended OSM data set for each building object

We analyse the created data set with two main objectives. First, we strive to
identify those variables from Table

Data pre-processing, model learning and model transfer workflow with BMu (upper-benchmark model); BMl (lower-benchmark model); BMrm (benchmark model with random match of interview locations with OSM building data); A (random forest model using eight predictors); B (random forest model using eight predictors); and model transfers d2E (learning with Dresden and predictions for Elbe), d2D (learning with Dresden and predictions for Danube), E2D (learning with Elbe and predictions for Danube) and D2E (learning with Danube and predictions for Elbe).

RFs are an extension of the classification and regression tree (CART) algorithm

For variable selection and predictive
model learning RFs provide a concept to quantify the importance of candidate
explanatory variables which allow for selecting the subset of most relevant
variables. RFs are also an efficient algorithm to learn predictive models from
heterogeneous data sets with complex interactions and with different scales like
continuous or categorical information

RF predictive model performance is sensitive to specifications of the algorithm
parameters mtry and ntree

The first step in model learning is the selection of variables to be used as
predictors in the model. The analysis of the Spearman’s
rank correlation between the variables gives a first insight into the linear
dependency structure of the data set. Furthermore, RF supports the evaluation
and ranking of potential predictors by quantification of variable importance
which also accounts for variable interaction effects. The importance of a
selected variable is evaluated by calculating the changes of the squared error
of the predictions when the values of that variable are randomly permuted in
the OOB sample. The increase of the average error will be larger for more
important variables and smaller for less important variables. On this basis it
is possible to decide which variables to include in a predictive model. The
outcomes of variable importance evaluations are sensitive to the RF algorithm
parameters mtry and ntree

Variable selection needs to be considered as an essential part of the model evaluation process. Therefore, candidate RF models using different numbers of variables are assessed in terms of predictive performance for independent data.

The OSM-based numerical spatial measures differentiate building form and shape
complexity. To gain further insights into the suitability of these variables
for flood vulnerability modelling, we incrementally add explanatory variables to
the learning data set. Based on the outcomes of variable importance ranking the
learning set is expanded variable by variable, and models of increasing
complexity are learned (cf. Table

Further, for an independent assessment of OSM-based vulnerability model performance we consider two benchmark models. We
argue that the set of CATI variables (Table 1) represents the most detailed
data set available for flood loss estimation of residential buildings

Model predictive performance is evaluated by comparing predicted (

Mean absolute error (MAE) quantifies the precision of model predictions, with
smaller values indicating higher precision:

Mean bias error (MBE) is a measure of accuracy, i.e. systematic deviation from
the observed value. Unbiased predictions yield a value of 0; underestimation results in negative; and overestimation in positive values:

Mean squared error (MSE) combines the variance of the model predictions and
their bias. Again, smaller values indicate better model performance:

The ensemble of model predictions from the RF models offers insight into
prediction uncertainty. This property is analysed by evaluating the 90 %
quantile range, i.e. the difference between the 5 % quantile and 95 % quantile in
relation to the median, as a measure of ensemble spread:

Reliability of model predictions is quantified in terms of the hit rate (HR)

HR calculates the ratio of observations within the 95 %–5 % quantile range of model predictions. For a reliable prediction HR should correspond to the expected nominal coverage of 0.9.

HR and QR

We investigate the question of whether the consistent data basis of OSM-derived
numerical spatial measures supports the transfer of flood vulnerability models
across regions by splitting the available data set into subsets
for different regions affected by major floods.
The CATI data are mainly located in the Elbe and Danube catchments in Germany,
which are the regions mostly affected by inundations and flood impacts.
This suggests a regional subdivision of
the empirical data set according to these river basins for the investigation of
spatial model transfer. In detail we partition the data set between the
metropolitan area of Dresden (Saxony), the Elbe catchment (Saxony,
Saxony-Anhalt and Thuringia) and the Danube catchment (Bavaria and
Baden-Württemberg); see Fig.

Computational experiments for transfer applications.

Random forest OOB errors are sensitive to the
choice of RF parameters mtry and ntree. From the variation of RF parameters we observe that
OOB errors decrease with smaller values
for mtry and larger numbers of trees in a forest (ntree); see Fig.

The coloured bands represent the 90 % quantile
range of OOB values from the 100 bootstrap repetitions for each RF algorithm configuration
and illustrate the inherent variability of input variables in the learning data
set.
The colour code distinguishes the number of variables used to determine splits at each
node (mtry). For mtry

Out-of-bag error for variations of mtry and ntree RF parameters. Colour bands represent the variation range of OOB errors obtained from 100 bootstrap repetitions.

The numerical spatial measures (Table

Spearman’s correlation of model variables (significance level of 1 %); non-significant correlations are crossed out.

The variable wst ranks first in the importance analysis (results not shown), which
confirms common knowledge in flood loss modelling

However, the outcome of the variable importance analysis does not suggest a clear
selection of features to be included in a predictive flood vulnerability model.
The model-predictive-performance-based assessment of variables uses an increasing
number of variables following their ranking order of variable importance in the RF modelling.
The predictive performance is quantified in terms of MAE, MBE and MSE (Eqs. 1, 2 and 3) for 100 bootstrap repetitions. While the MAE is decreasing when additional
variables are used with an overall minimum for a model using six variables,
including more than six variables tends to increase MAE again (Fig.

Predictive performance of models using an increasing number of variables in order of their importance. Smaller MAE and MSE values and MBE values close to 0 indicate better performance; cf. Eqs. (1)–(3).

Looking into the sharpness of model predictions, the quantile range (QR

Model performance metrics for models using an increasing number of variables arranged in the order of wst, PARatio, RadGyras, Area, LinSegInd, BoundRatio, Perimeter, DegrComp, FracDimInd and ShapeIndex. Best performance values and selected models are in bold.

On the basis of these assessments two model alternatives are selected for further analysis: model A using eight variables, as it provides the most reliable model predictions, and model B using six variables, which provide the highest precision and balance between accuracy and precision. In detail model B uses the variables wst, PARatio, RadGyras, Area, LinSegInd and BoundRatio. Model A, in addition, uses Perimeter and DegrComp as predictors.

The OSM models A and B are benchmarked with a model that uses all information
available from the CATI surveys as an upper benchmark (BMu) and a model that
uses only water depth as predictor as a lower benchmark (BMl). The performance
statistics achieved by models A and B for the complete data set (all events and
regions) are slightly inferior to BMu but clearly better than the outcomes of
BMl (Fig.

Performance metrics of OSM-based models and benchmark models.

Model precision, accuracy and reliability performance metrics for OSM-based models and benchmark models.

The predictive performance of RF models is tested in regional-transfer
applications. For this purpose, the RF models A and B as well as the benchmark
models BMu and BMl, as specified in the previous section, are learned using
regional subsets of the data and applied to predict flood losses in a
different region; see Sect. 3.4 and Table

Model performance metrics in regional transfer. Models A and B
based on spatial numerical measures calculated for OSM building footprints;
benchmark models BMl and BMu based on CATI survey data. Transfer
experiments d2E, d2D, E2D and D2E as described in Table

With 105 records the Danube data set is the smallest sub-sample. It has a
smaller variability and range of values for most numerical spatial measures in
comparison to the Dresden and Elbe regional sets (Fig.

Scatterplots of numerical spatial measures and relative loss in regional sub-samples (Danube, Dresden and Elbe).

The geometric
properties of the flood-affected residential buildings in the Danube region
seem to differ from the affected residential buildings in the Elbe region.
In the Danube subset, the area and perimeter of buildings tend to be smaller than in the Elbe region.
Also, the values for spatial measures representing building shape complexity, for instance RadGyras, DegrComp and BoundRatio, indicate more compact building footprints in the
Danube region than in the Elbe region.
These differences can be attributed to different socio-economic characteristics as well as
building practices in former East and West Germany and regional differences in building types

The transfer of flood vulnerability models to regions other than those for which they have been developed often comes with reduced predictive performance. In this study we investigated the suitability of numerical spatial measures calculated for residential-building footprints, which are accessible from OpenStreetMap, to predict flood damage. Further we tested potential benefits from using this widely available and consistent input data source for the transfer of vulnerability models across regions. We develop a new data set based on OpenStreetMap data, which comprises variables representing building footprint dimensions and shape complexity, and we devise novel flood vulnerability models for residential buildings.

The geometric characteristics of building footprints serve as proxy variables for building resistance to flood impacts and prove useful for flood loss estimation. These model input variables are easily extracted by an automated process applicable to every type of building polygon. Hence, the models can be applied to areas where information about the footprint geometry of residential buildings is available. Also other data sources, e.g. cadastral data or data derived from remote sensing, can be used besides the OpenStreetMap data source. While the variables derived from building footprints ensure consistency and support transferability of models, the models remain context specific and should only be transferred to regions with comparable building geometric features as the learning data set.

The vulnerability models have been validated using empirical data of relative loss to residential buildings. Further, a benchmark comparison of the models has been conducted in spatial-transfer applications. The models give comparable performance to alternative multi-variable models, which use comprehensive and detailed information about preparedness, socio-economic status and other aspects of building vulnerability. In comparison to a model which uses only water depth as a predictor, they reduce model prediction errors (MAE by 20 % and MSE by 25 %) and increase the reliability of model predictions by a factor of 1.4.

OpenStreetMap is a highly popular and evolving data source with constantly increasing completeness and up-to-date data. In the future, the attributes of residential buildings are expected to provide additional details which are of interest for the characterization of building resistance to flooding. This includes for instance information about the building type, roof type, number of floors and building material and opens up further possibilities to refine the variables used for vulnerability modelling. These data could be further amended with other open-data sources including socio-economic statistical data. In view of a large variability of flood loss on the individual-building level, vulnerability modelling for individual buildings remains challenging and is subject to large uncertainty. Advances to the understanding of damage processes and the improvement of flood vulnerability modelling hence require an improved and extended monitoring of flood losses.

Flood damage data of the 2005, 2006, 2010, 2011 and 2013 events along with instructions
on how to access the data are available via the German flood damage database,
HOWAS21 (

OSM is an open-data project, and the cartographic information can be
downloaded, altered and redistributed under the Open Data Commons Open Database
License (ODbL)

In the presented study, the geographic data were
processed in PostgreSQL 12.2 with the PostGIS 3.0.1 extension and R version 3.6.3
(29 February 2020)

MC and KS conceived and designed the study. MC prepared and analysed the data with support from MS and KS. MC and KS wrote the first draft of the paper. HK helped guide the research through technical discussions. All authors reviewed the draft of the paper and contributed to the final version.

Authors Heidi Kreibich and Kai Schröter are
members of the editorial board of

This article is part of the special issue “Groundbreaking technologies, big data, and innovation for disaster risk modelling and reduction”. It is not associated with a conference.

The authors gratefully acknowledge the support by the German Research Network Natural Disasters (German Ministry of Education and Research (BMBF), no. 01SFR9969/5); the MEDIS project (BMBF; no. 0330688); the project “Hochwasser 2013” (BMBF; no. 13N13017); and a joint venture between the GFZ German Research Centre for Geosciences, the University of Potsdam and the Deutsche Rückversicherung Aktiengesellschaft (Düsseldorf) for the collection of empirical damage data using computer-aided telephone interviews. The authors further would like to thank Stefan Lüdtke and Danijel Schorlemmer (both from GFZ) for technical support with OSM data.

This research has been supported by Horizon 2020 (H2020_Insurance, grant no. 730381) and the EIT Climate-KIC (grant no. TC2018B_4.7.3-SAFERPL_P430-1A KAVA2 4.7.3).The article processing charges for this open-access publication were covered by a Research Centre of the Helmholtz Association.

This paper was edited by Carmine Galasso and reviewed by four anonymous referees.