An Integrated Model for Ex-ante Evaluation of Flood Damage to Residential Building

2016 ◽  
pp. 157-170 ◽  
Author(s):  
Marco Mancini ◽  
Gabriele Lombardi ◽  
Sergio Mattia ◽  
Alessandra Oppio ◽  
Francesca Torrieri
Keyword(s):  
Residential Building ◽  
Flood Damage ◽  
Integrated Model ◽  
Ex Ante

scholarly journals TECHNOLOGY SHOCKS AND HOURS WORKED: A FRACTIONAL INTEGRATION PERSPECTIVE

2009 ◽  
Vol 13 (5) ◽  
pp. 580-604 ◽  
Author(s):  
Luis Alberiko Gil-Alana ◽  
Antonio Moreno
Keyword(s):  
Dynamic Response ◽  
Fractional Integration ◽  
Integrated Model ◽  
Technology Shocks ◽  
Hours Worked ◽  
Ex Ante ◽  
The Hours ◽  
Technology Shock

Previous research has found that the dynamic response of hours worked to a technology shock crucially depends on whether the hours variable is assumed to be an I(0) or an I(1) variable ex ante. In this paper we employ a multivariate fractionally integrated model that allows us to simultaneously estimate the order of integration of hours worked and its dynamic response to a technology shock. Our evidence lends support to the hypothesis that hours fall in response to a positive technology shock.

scholarly journals Data-mining for multi-variable flood damage modelling with limited data

2017 ◽  
Author(s):  
Dennis Wagenaar ◽  
Jurjen de Jong ◽  
Laurens M. Bouwer
Keyword(s):  
Data Mining ◽  
Bayesian Network ◽  
Water Depth ◽  
Damage Assessment ◽  
Damage Model ◽  
Residential Building ◽  
Regression Trees ◽  
Flood Damage ◽  
Data Mining Techniques ◽  
Flood Damage Assessment

Abstract. Flood damage assessment is usually done with damage curves only dependent on the water depth. Recent studies have shown that data-mining techniques applied to a multi-dimensional dataset can produce significantly better flood damage estimates. However, creating and applying a multi-variable flood damage model requires an extensive dataset, which is rarely available and this can limit the application of these new techniques. In this paper we enrich a dataset of residential building and content damages from the Meuse flood of 1993 in the Netherlands, to make it suitable for multi-variable flood damage assessment. Results from 2D flood simulations are used to add information on flow velocity, flood duration and the return period to the dataset, and cadastre data is used to add information on building characteristics. Next, several statistical approaches are used to create multi-variable flood damage models, including regression trees, bagging regression trees, random forest, and a Bayesian network. Validation on data points from a test set shows that the enriched dataset in combination with the data-mining techniques delivers a significant improvement over a simple model only based on the water depth. We find that with our dataset, the trees based methods perform better than the Bayesian Network.

scholarly journals Are OpenStreetMap building data useful for flood vulnerability modelling?

2021 ◽  
Vol 21 (2) ◽  
pp. 643-662
Author(s):  
Marco Cerri ◽  
Max Steinhausen ◽  
Heidi Kreibich ◽  
Kai Schröter
Keyword(s):  
Data Model ◽  
Mean Squared Error ◽  
Residential Buildings ◽  
Residential Building ◽  
Predictive Performance ◽  
Flood Damage ◽  
Model Learning ◽  
Data Set ◽  
Flood Vulnerability ◽  
Spatial Measures

Abstract. 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.

scholarly journals On the Influence of the Main Floor Layout of Buildings in Economic Flood Risk Assessment: Results from Central Spain

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 670 ◽  
Author(s):  
Julio Garrote ◽  
Nestor Bernal
Keyword(s):  
Complex Analysis ◽  
Complete Solution ◽  
Residential Building ◽  
Flood Damage ◽  
Damage Function ◽  
Economic Damage ◽  
Damage Estimation ◽  
Damage Functions ◽  
Essential Variable ◽  
Correct Estimation

Multiple studies have been carried out on the correct estimation of the damages (direct tangible losses) associated with floods. However, the complex analysis and the multitude of variables conditioning the damage estimation, as well as the uncertainty in their estimation, make it difficult, even today, to reach one single, complete solution to this problem. In no case has the influence that the topographic relationship between the main floor of a residential building and the surrounding land have in the estimation of flood economic damage been analysed. To carry out this analysis, up to a total of 28 magnitude–damage functions (with different characteristics and application scales) were selected on which the effect of over-elevation and under-elevation of the main floor of the houses was simulated (at intervals of 20 cm, between −0.6 and +1 metre). According to each of the two trends, an overestimation or underestimation of flood damage was observed. This pattern was conditioned by the specific characteristics of each magnitude–damage function, meaning that the percentage of damage became asymptotic from a certain flow depth value. In a real scenario, the consideration of this variable (as opposed to its non-consideration) causes an average variation in the damage estimation around 30%. Based on these results, the analysed variable can be considered as (1) another main source of uncertainty in the correct estimation of flood damage, and (2) an essential variable to take into account in a flood damage analysis for the correct estimation of loss.

scholarly journals Are new open building data useful for flood vulnerability modelling?

2020 ◽  
Author(s):  
Marco Cerri ◽  
Max Steinhausen ◽  
Heidi Kreibich ◽  
Kai Schröter
Keyword(s):  
Residential Buildings ◽  
Open Data ◽  
Residential Building ◽  
Predictive Performance ◽  
Flood Damage ◽  
Building Vulnerability ◽  
Damage Functions ◽  
Data Set ◽  
Flood Vulnerability ◽  
Spatial Measures

Abstract. 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 also often have problems to predict 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 question of 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 to transfer models across regions. We develop a new data set by calculating numerical spatial measures for residential building footprint geometries and combine these variables with an empirical data set of observed flood damage. From this data set random forest regression models are learned using regional sub-sets 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 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. However, our results show that using numerical spatial measures derived from OpenStreetMap building geometries 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, openly accessible, and thus make it easier and more cost-effective to transfer vulnerability models to other regions.

Ex ante coherence shifts

2020 ◽  
Vol 43 ◽  
Author(s):  
Dan Simon ◽  
Keith J. Holyoak
Keyword(s):  
Course Of Action ◽  
Ex Ante ◽  
Rational Reasoning

Abstract Cushman characterizes rationalization as the inverse of rational reasoning, but this distinction is psychologically questionable. Coherence-based reasoning highlights a subtler form of bidirectionality: By distorting task attributes to make one course of action appear superior to its rivals, a patina of rationality is bestowed on the choice. This mechanism drives choice and action, rather than just following in their wake.

Multilinguistic Components of Spelling: An Overview

2013 ◽  
Vol 20 (4) ◽  
pp. 124-128 ◽  
Author(s):  
Angela Barber
Keyword(s):  
Integrated Model ◽  
Literacy Skills ◽  
Speech Language Pathologists ◽  
Language System ◽  
Adequate Information ◽  
Intervention Plan ◽  
Semantic Awareness

Spelling is a window into a student's individual language system and, therefore, canprovide clues into the student's understanding, use, and integration of underlyinglinguistic skills. Speech-language pathologists (SLPs) should be involved in improvingstudents' literacy skills, including spelling, though frequently available measures ofspelling do not provide adequate information regarding critical underlying linguistic skillsthat contribute to spelling. This paper outlines a multilinguistic, integrated model of wordstudy (Masterson & Apel, 2007) that highlights the important influences of phonemicawareness, orthographic pattern awareness, semantic awareness, morphologicalawareness and mental graphemic representations on spelling. An SLP can analyze anindividual's misspellings to identify impairments in specific linguistic components andthen develop an individualized, appropriate intervention plan tailored to a child's uniquelinguistic profile, thus maximizing intervention success.

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