scholarly journals Influence of flood frequency on residential building losses

2010 ◽  
Vol 10 (10) ◽  
pp. 2145-2159 ◽  
Author(s):  
F. Elmer ◽  
A. H. Thieken ◽  
I. Pech ◽  
H. Kreibich
Keyword(s):  
Residential Buildings ◽  
Statistical Error ◽  
Residential Building ◽  
Water Levels ◽  
Recurrence Interval ◽  
Flood Events ◽  
Flood Loss ◽  
Loss Data ◽  
Loss Models ◽  
Loss Modelling

Abstract. For the purpose of flood risk analysis, reliable loss models are an indispensable need. The most common models use stage-damage functions relating damage to water depth. They are often derived from empirical flood loss data (i.e. loss data collected after a flood event). However, object specific loss data (e.g. losses of single residential buildings) from recent flood events in Germany showed higher average losses in less probable events, regardless of actual water level. Hence, models that were derived from such data tend to overestimate losses caused by more probable events. Therefore, it is the aim of the study to analyse the relation between flood damage and recurrence interval and to propose a method for considering recurrence interval in flood loss modelling. The survey was based on residential building loss data (n=2158) of recent flood events in 2002, 2005 and 2006 in Germany and on-site recurrence interval of the respective events. We discovered a highly significant positive correlation between loss extent and recurrence interval for classified water levels as well as increasing average losses for longer recurrence intervals within each class. The application of principal component analysis revealed the interrelation between factors that influence the damage extent directly or indirectly, and recurrence interval. No single factor or component could be identified that explained the influence of recurrence interval, which led to the conclusion that recurrence interval cannot substitute, but complement other damage influencing factors in flood loss modelling approaches. Finally, a method was developed to include recurrence interval in typical flood loss models and make them applicable to a wider range of flood events. Validation including statistical error analysis showed that the modified models improve loss estimates in comparison to traditional approaches. The proposed multi-parameter model FLEMOps+r performs particularly well.

scholarly journals Development and assessment of uni- and multi-variable flood loss models for Emilia-Romagna (Italy)

2017 ◽  
Author(s):  
Francesca Carisi ◽  
Kai Schröter ◽  
Alessio Domeneghetti ◽  
Heidi Kreibich ◽  
Attilio Castellarin
Keyword(s):  
Residential Buildings ◽  
Economic Value ◽  
Flood Damage ◽  
Data Sets ◽  
Local Data ◽  
Data Set ◽  
Damage Models ◽  
Flood Loss ◽  
Loss Data ◽  
Loss Models

Abstract. Simplified flood loss models are one important source of uncertainty in flood risk assessments. Many countries experience sparseness or absence of comprehensive high-quality flood loss data sets which is often rooted in a lack of protocols and reference procedures for compiling loss data sets after flood events. Such data are an important reference for developing and validating flood loss models. We consider the Secchia river flood event of January 2014, when a sudden levee-breach caused the inundation of nearly 52 km2 in Northern Italy. For this event we compiled a comprehensive flood loss data set of affected private households including buildings footprint, economic value, damages to contents, etc. based on information collected by local authorities after the event. By analysing this data set we tackle the problem of flood damage estimation in Emilia-Romagna (Italy) by identifying empirical uni- and multi-variable loss models for residential buildings and contents. The accuracy of the proposed models is compared with those of several flood-damage models reported in the literature, providing additional insights on the transferability of the models between different contexts. Our results show that (1) even simple uni-variable damage models based on local data are significantly more accurate than literature models derived for different contexts; (2) multi-variable models that consider several explanatory variables outperform uni-variable models which use only water depth. However, multi-variable models can only be effectively developed and applied if sufficient and detailed information is available.

scholarly journals Adaptability and transferability of flood loss functions in residential areas

2013 ◽  
Vol 1 (4) ◽  
pp. 3485-3527 ◽  
Author(s):  
H. Cammerer ◽  
A. H. Thieken ◽  
J. Lammel
Keyword(s):  
Flood Risk ◽  
Residential Buildings ◽  
Model Development ◽  
Loss Functions ◽  
Residential Areas ◽  
Damage Functions ◽  
Residential Sector ◽  
Geographical Regions ◽  
Flood Loss ◽  
Loss Data

Abstract. Flood loss modeling is an important component within flood risk assessments. Traditionally, stage-damage functions are used for the estimation of direct monetary damage to buildings. Although it is known that such functions are governed by large uncertainties, they are commonly applied – even in different geographical regions – without further validation, mainly due to the lack of data. Until now, little research has been done to investigate the applicability and transferability of such damage models to other regions. In this study, the last severe flood event in the Austrian Lech Valley in 2005 was simulated to test the performance of various damage functions for the residential sector. In addition to common stage-damage curves, new functions were derived from empirical flood loss data collected in the aftermath of recent flood events in the neighboring Germany. Furthermore, a multi-parameter flood loss model for the residential sector was adapted to the study area and also evaluated by official damage data. The analysis reveals that flood loss functions derived from related and homogenous regions perform considerably better than those from more heterogeneous datasets. To illustrate the effect of model choice on the resulting uncertainty of damage estimates, the current flood risk for residential areas was assessed. In case of extreme events like the 300 yr flood, for example, the range of losses to residential buildings between the highest and the lowest estimates amounts to a factor of 18, in contrast to properly validated models with a factor of 2.3. Even if the risk analysis is only performed for residential areas, more attention should be paid to flood loss assessments in future. To increase the reliability of damage modeling, more loss data for model development and validation are needed.

scholarly journals Adaptability and transferability of flood loss functions in residential areas

2013 ◽  
Vol 13 (11) ◽  
pp. 3063-3081 ◽  
Author(s):  
H. Cammerer ◽  
A. H. Thieken ◽  
J. Lammel
Keyword(s):  
Flood Risk ◽  
Loss Functions ◽  
Flood Events ◽  
Residential Areas ◽  
Damage Functions ◽  
Residential Sector ◽  
Geographical Regions ◽  
Flood Loss ◽  
Loss Data ◽  
Damage Data

Abstract. Flood loss modeling is an important component within flood risk assessments. Traditionally, stage-damage functions are used for the estimation of direct monetary damage to buildings. Although it is known that such functions are governed by large uncertainties, they are commonly applied – even in different geographical regions – without further validation, mainly due to the lack of real damage data. Until now, little research has been done to investigate the applicability and transferability of such damage models to other regions. In this study, the last severe flood event in the Austrian Lech Valley in 2005 was simulated to test the performance of various damage functions from different geographical regions in Central Europe for the residential sector. In addition to common stage-damage curves, new functions were derived from empirical flood loss data collected in the aftermath of recent flood events in neighboring Germany. Furthermore, a multi-parameter flood loss model for the residential sector was adapted to the study area and also evaluated with official damage data. The analysis reveals that flood loss functions derived from related and more similar regions perform considerably better than those from more heterogeneous data sets of different regions and flood events. While former loss functions estimate the observed damage well, the latter overestimate the reported loss clearly. To illustrate the effect of model choice on the resulting uncertainty of damage estimates, the current flood risk for residential areas was calculated. In the case of extreme events like the 300 yr flood, for example, the range of losses to residential buildings between the highest and the lowest estimates amounts to a factor of 18, in contrast to properly validated models with a factor of 2.3. Even if the risk analysis is only performed for residential areas, our results reveal evidently that a carefree model transfer in other geographical regions might be critical. Therefore, we conclude that loss models should at least be selected or derived from related regions with similar flood and building characteristics, as far as no model validation is possible. To further increase the general reliability of flood loss assessment in the future, more loss data and more comprehensive loss data for model development and validation are needed.

scholarly journals Development and assessment of uni- and multivariable flood loss models for Emilia-Romagna (Italy)

2018 ◽  
Vol 18 (7) ◽  
pp. 2057-2079 ◽  
Author(s):  
Francesca Carisi ◽  
Kai Schröter ◽  
Alessio Domeneghetti ◽  
Heidi Kreibich ◽  
Attilio Castellarin
Keyword(s):  
Residential Buildings ◽  
Flood Damage ◽  
Local Data ◽  
Explanatory Variables ◽  
Damage Models ◽  
Private Households ◽  
Flood Loss ◽  
Multivariable Models ◽  
Loss Models ◽  
Flood Damage Estimation

Abstract. Flood loss models are one important source of uncertainty in flood risk assessments. Many countries experience sparseness or absence of comprehensive high-quality flood loss data, which is often rooted in a lack of protocols and reference procedures for compiling loss datasets after flood events. Such data are an important reference for developing and validating flood loss models. We consider the Secchia River flood event of January 2014, when a sudden levee breach caused the inundation of nearly 52 km2 in northern Italy. After this event local authorities collected a comprehensive flood loss dataset of affected private households including building footprints and structures and damages to buildings and contents. The dataset was enriched with further information compiled by us, including economic building values, maximum water depths, velocities and flood durations for each building. By analyzing this dataset we tackle the problem of flood damage estimation in Emilia-Romagna (Italy) by identifying empirical uni- and multivariable loss models for residential buildings and contents. The accuracy of the proposed models is compared with that of several flood damage models reported in the literature, providing additional insights into the transferability of the models among different contexts. Our results show that (1) even simple univariable damage models based on local data are significantly more accurate than literature models derived for different contexts; (2) multivariable models that consider several explanatory variables outperform univariable models, which use only water depth. However, multivariable models can only be effectively developed and applied if sufficient and detailed information is available.

Flood loss models for residential buildings, based on the 2013 Colorado floods

2016 ◽  
Vol 85 (2) ◽  
pp. 977-1003 ◽  
Author(s):  
Derya Deniz ◽  
Erin E. Arneson ◽  
Abbie B. Liel ◽  
Shideh Dashti ◽  
Amy N. Javernick-Will
Keyword(s):  
Residential Buildings ◽  
Flood Loss ◽  
Loss Models

scholarly journals Probabilistic Flood Loss Models for Companies

2020 ◽  
Author(s):  
Lukas Schoppa ◽  
Tobias Sieg ◽  
Kristin Vogel ◽  
Gert Zöller ◽  
Heidi Kreibich
Keyword(s):  
Risk Assessment ◽  
Flood Risk ◽  
Loss Estimation ◽  
Flood Risk Assessment ◽  
Damage Functions ◽  
Flood Loss ◽  
Loss Data ◽  
Multivariable Models ◽  
Loss Models ◽  
Estimation Models

<p>Flood risk assessment strongly relies on accurate and reliable estimation of monetary flood loss. Conventionally, this involves univariable deterministic stage-damage functions. Recent advancements in the field promote the use of multivariable probabilistic loss estimation models which consider damage controlling variables beyond inundation depth. Although companies contribute significantly to total loss figures, multivariable probabilistic modeling approaches for companies are lacking. Scarce data and heterogeneity among companies impedes the development of novel company flood loss models.</p><p>We present three multivariable flood loss estimation models for companies that intrinsically quantify prediction uncertainty. Based on object-level loss data (n=1306), we comparatively evaluate the predictive performance of Bayesian networks, Bayesian regression and random forest in relation to established stage-damage functions. The company loss data stems from four post-event surveys after major floods in Germany between 2002 and 2013 and comprises information on flood intensity, company characteristics and private precaution. We examine the performance of the candidate models separately for losses to building, equipment, and goods and stock. Plausibility checks show that the multivariable models are able to identify and reproduce essential relationships of the flood damage processes from the data. The comparison of the prediction capacity reveals that the proposed models outperform stage-damage functions clearly while differences among the multivariable models are small. Even though the presented models improve the accuracy of loss predictions, wide predictive distributions underline the necessity for the quantification of predictive uncertainty. This applies particularly to companies, for which the heterogeneity and variation in the loss data are more pronounced than for private households. Due to their probabilistic nature, the presented multivariable models contribute towards a transparent treatment of uncertainties in flood risk assessment.</p>

scholarly journals Up-scaling of multi-variable flood loss models from objects to land use units at the meso-scale

2016 ◽  
Vol 373 ◽  
pp. 179-182 ◽  
Author(s):  
Heidi Kreibich ◽  
Kai Schröter ◽  
Bruno Merz
Keyword(s):  
Land Use ◽  
Large Scale ◽  
Scale Validation ◽  
Damage Functions ◽  
Micro Scale ◽  
Flood Loss ◽  
Loss Models ◽  
Loss Modelling ◽  
Meso Scale ◽  
Up Scaling

Abstract. Flood risk management increasingly relies on risk analyses, including loss modelling. Most of the flood loss models usually applied in standard practice have in common that complex damaging processes are described by simple approaches like stage-damage functions. Novel multi-variable models significantly improve loss estimation on the micro-scale and may also be advantageous for large-scale applications. However, more input parameters also reveal additional uncertainty, even more in upscaling procedures for meso-scale applications, where the parameters need to be estimated on a regional area-wide basis. To gain more knowledge about challenges associated with the up-scaling of multi-variable flood loss models the following approach is applied: Single- and multi-variable micro-scale flood loss models are up-scaled and applied on the meso-scale, namely on basis of ATKIS land-use units. Application and validation is undertaken in 19 municipalities, which were affected during the 2002 flood by the River Mulde in Saxony, Germany by comparison to official loss data provided by the Saxon Relief Bank (SAB).In the meso-scale case study based model validation, most multi-variable models show smaller errors than the uni-variable stage-damage functions. The results show the suitability of the up-scaling approach, and, in accordance with micro-scale validation studies, that multi-variable models are an improvement in flood loss modelling also on the meso-scale. However, uncertainties remain high, stressing the importance of uncertainty quantification. Thus, the development of probabilistic loss models, like BT-FLEMO used in this study, which inherently provide uncertainty information are the way forward.

Tenants' Satisfaction in High Residential Building

2020 ◽  
Vol 17 (1) ◽  
pp. 41
Author(s):  
UMMU SHOLEHAH MOHD NOR
Keyword(s):  
Urban Areas ◽  
Residential Buildings ◽  
Low Cost ◽  
Residential Building ◽  
Real Estate Market ◽  
The Real Estate ◽  
Level Of Satisfaction ◽  
Considerable Impact ◽  
Residential Living ◽  
Medium Cost

High residential living in Malaysia has not been widely given a significant emphasises in literature despite its increasing scale and significance in the real estate market. The significance of high rise is mainly due the increasing rate of migration from rural to urban. It is estimated a total of 77.2 percent of the Malaysian population lived in urban areas in 2020. Approximately, 30 percent of this urban population lives in strata housing. These percentages are predicted to continue to increase in the future. The emergence of high residential building has been argued as confronting various problems which has considerable impact on this life style. Satisfaction is an important outcome of living in one’s dwelling, although it is not the only consideration. High residential building in Malaysia encountered numerous problems in term of management aspects, legislation aspects, and residents’ satisfaction. The purpose of this paper is to investigate the tenants’ satisfaction living in high residential buildings in Klang Valley. The questionnaires survey is conducted amongst 276 tenants at low cost and medium cost HRB using random sampling in HRB located at areas under jurisdiction Dewan Bandaraya Kuala Lumpur (DBKL), Majlis Bandaraya Subang Jaya (MBSJ), Majlis Bandaraya Shah Alam (MBSA), Majlis Bandaraya Subang Jaya (MBSJ), Majlis Perbandaran Selayang (MPS) and Majlis Perbandaran Ampang Jaya (MPAJ). The result from this study shows that tenant in medium cost residential building are more satisfied in term of facilities and management as compared to tenants in low cost residential building. Tenants also not disclosed to the existing act and procedure related to high residential building. In conclusion, this study suggested the Local Authority to emphasise the role of tenant. These recommendation hopefully will increase the level of satisfaction amongst the residents in HRB.

scholarly journals Optimal Control Strategies for Switchable Transparent Insulation Systems Applied to Smart Windows for US Residential Buildings

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2917
Author(s):  
Mohammad Dabbagh ◽  
Moncef Krarti
Keyword(s):  
Energy Use ◽  
Control Strategies ◽  
Residential Buildings ◽  
Residential Building ◽  
Optimization Approach ◽  
Optimal Controls ◽  
Peak Demand ◽  
Smart Windows ◽  
Insulation Systems ◽  
Rule Set

This paper evaluates the potential energy use and peak demand savings associated with optimal controls of switchable transparent insulation systems (STIS) applied to smart windows for US residential buildings. The optimal controls are developed based on Genetic Algorithm (GA) to identify the automatic settings of the dynamic shades. First, switchable insulation systems and their operation mechanisms are briefly described when combined with smart windows. Then, the GA-based optimization approach is outlined to operate switchable insulation systems applied to windows for a prototypical US residential building. The optimized controls are implemented to reduce heating and cooling energy end-uses for a house located four US locations, during three representative days of swing, summer, and winter seasons. The performance of optimal controller is compared to that obtained using simplified rule-based control sets to operate the dynamic insulation systems. The analysis results indicate that optimized controls of STISs can save up to 81.8% in daily thermal loads compared to the simplified rule-set especially when dwellings are located in hot climates such as that of Phoenix, AZ. Moreover, optimally controlled STISs can reduce electrical peak demand by up to 49.8% compared to the simplified rule-set, indicating significant energy efficiency and demand response potentials of the SIS technology when applied to US residential buildings.

scholarly journals Ensemble Prediction Approach Based on Learning to Statistical Model for Efficient Building Energy Consumption Management

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 405
Author(s):  
Anam Nawaz Khan ◽  
Naeem Iqbal ◽  
Rashid Ahmad ◽  
Do-Hyeun Kim
Keyword(s):  
Energy Consumption ◽  
Statistical Model ◽  
Residential Buildings ◽  
Residential Building ◽  
Experimental Results ◽  
Consumption Patterns ◽  
Ensemble Prediction ◽  
Short Term ◽  
Term Energy ◽  
Prediction Approach

With the development of modern power systems (smart grid), energy consumption prediction becomes an essential aspect of resource planning and operations. In the last few decades, industrial and commercial buildings have thoroughly been investigated for consumption patterns. However, due to the unavailability of data, the residential buildings could not get much attention. During the last few years, many solutions have been devised for predicting electric consumption; however, it remains a challenging task due to the dynamic nature of residential consumption patterns. Therefore, a more robust solution is required to improve the model performance and achieve a better prediction accuracy. This paper presents an ensemble approach based on learning to a statistical model to predict the short-term energy consumption of a multifamily residential building. Our proposed approach utilizes Long Short-Term Memory (LSTM) and Kalman Filter (KF) to build an ensemble prediction model to predict short term energy demands of multifamily residential buildings. The proposed approach uses real energy data acquired from the multifamily residential building, South Korea. Different statistical measures are used, such as mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and R2 score, to evaluate the performance of the proposed approach and compare it with existing models. The experimental results reveal that the proposed approach predicts accurately and outperforms the existing models. Furthermore, a comparative analysis is performed to evaluate and compare the proposed model with conventional machine learning models. The experimental results show the effectiveness and significance of the proposed approach compared to existing energy prediction models. The proposed approach will support energy management to effectively plan and manage the energy supply and demands of multifamily residential buildings.

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