Predicting the hurricane damage ratio of commercial buildings by claim payout from Hurricane Ike

Abstract. The increasing occurrence of natural disaster events and related damages have led to a growing demand for models that predict financial loss. Although considerable research has studied the financial losses related to natural disaster events, and has found significant predictors, there has not yet been a comprehensive study that addresses the relationship among the vulnerabilities, natural disasters, and economic losses of the individual buildings. This study identified hurricanes and their vulnerability indicators in order to establish a metric to predict the related financial loss. We identify hurricane-prone areas by imaging the spatial distribution of the losses and vulnerabilities. This study utilized a Geographical Information System (GIS) to combine and produce spatial data, as well as a multiple linear regression method, to establish a hurricane damage prediction model. As the dependent variable, we utilized the following ratio to predict the real pecuniary loss: the value of the Texas Windstorm Insurance Association (TWIA) claim payout divided by the appraised values of the buildings. As independent variables, we selected the hurricane indicators and vulnerability indicators of the built environment and the geographical features. The developed statistical model and results can be used as important guidelines by insurance companies, government agencies, and emergency planners for predicting hurricane damage.

Download Full-text

Predicting the Texas Windstorm Insurance Association claim payout of commercial buildings from Hurricane Ike

Natural Hazards and Earth System Sciences Discussions ◽

10.5194/nhessd-1-3813-2013 ◽

2013 ◽

Vol 1 (4) ◽

pp. 3813-3855

Author(s):

J. M. Kim ◽

P. K. Woods ◽

Y. J. Park ◽

K. Son

Keyword(s):

Public Awareness ◽

Insurance Companies ◽

Economic Losses ◽

Economic Damage ◽

Linear Regression Method ◽

Vulnerability Factors ◽

Hurricane Damage ◽

Vulnerability Indicators ◽

Insured Loss ◽

The Relationship

Abstract. Following growing public awareness of the danger from hurricanes and tremendous demands for analysis of loss, many researchers have conducted studies to develop hurricane damage analysis methods. Although researchers have identified the significant indicators, there currently is no comprehensive research for identifying the relationship among the vulnerabilities, natural disasters, and economic losses associated with individual buildings. To address this lack of research, this study will identify vulnerabilities and hurricane indicators, develop metrics to measure the influence of economic losses from hurricanes, and visualize the spatial distribution of vulnerability to evaluate overall hurricane damage. This paper has utilized the Geographic Information System to facilitate collecting and managing data, and has combined vulnerability factors to assess the financial losses suffered by Texas coastal counties. A multiple linear regression method has been applied to develop hurricane economic damage predicting models. To reflect the pecuniary loss, insured loss payment was used as the dependent variable to predict the actual financial damage. Geographical vulnerability indicators, built environment vulnerability indicators, and hurricane indicators were all used as independent variables. Accordingly, the models and findings may possibly provide vital references for government agencies, emergency planners, and insurance companies hoping to predict hurricane damage.

Download Full-text

Ensemble of ground subsidence hazard maps using fuzzy logic

Open Geosciences ◽

10.2478/s13533-012-0175-y ◽

2014 ◽

Vol 6 (2) ◽

Cited By ~ 13

Author(s):

Inhye Park ◽

Jiyeong Lee ◽

Lee Saro

Keyword(s):

Spatial Data ◽

Ensemble Methods ◽

Geographical Information ◽

Ground Subsidence ◽

Hazard Maps ◽

Subsidence Area ◽

Ensemble Techniques ◽

Ann Models ◽

Artificial Neural Network Ann ◽

The Individual

AbstractHazard maps of ground subsidence around abandoned underground coal mines (AUCMs) in Samcheok, Korea, were constructed using fuzzy ensemble techniques and a geographical information system (GIS). To evaluate the factors related to ground subsidence, a spatial database was constructed from topographic, geologic, mine tunnel, land use, groundwater, and ground subsidence maps. Spatial data, topography, geology, and various ground-engineering data for the subsidence area were collected and compiled in a database for mapping ground-subsidence hazard (GSH). The subsidence area was randomly split 70/30 for training and validation of the models. The relationships between the detected ground-subsidence area and the factors were identified and quantified by frequency ratio (FR), logistic regression (LR) and artificial neural network (ANN) models. The relationships were used as factor ratings in the overlay analysis to create ground-subsidence hazard indexes and maps. The three GSH maps were then used as new input factors and integrated using fuzzy-ensemble methods to make better hazard maps. All of the hazard maps were validated by comparison with known subsidence areas that were not used directly in the analysis. As the result, the ensemble model was found to be more effective in terms of prediction accuracy than the individual model.

Download Full-text

Quantification of PV Power and Economic Losses Due to Soiling in Qatar

Sustainability ◽

10.3390/su13063364 ◽

2021 ◽

Vol 13 (6) ◽

pp. 3364

Author(s):

Amr Zeedan ◽

Abdulaziz Barakeh ◽

Khaled Al-Fakhroo ◽

Farid Touati ◽

Antonio S. P. Gonzales

Keyword(s):

Output Power ◽

Power Plants ◽

Economic Loss ◽

Economic Losses ◽

Energy Yield ◽

Solar Pv ◽

Financial Loss ◽

Grid Systems ◽

Dust Density

Soiling losses of photovoltaic (PV) panels due to dust lead to a significant decrease in solar energy yield and result in economic losses; this hence poses critical challenges to the viability of PV in smart grid systems. In this paper, these losses are quantified under Qatar’s harsh environment. This quantification is based on experimental data from long-term measurements of various climatic parameters and the output power of PV panels located in Qatar University’s Solar facility in Doha, Qatar, using a customized measurement and monitoring setup. A data processing algorithm was deliberately developed and applied, which aimed to correlate output power to ambient dust density in the vicinity of PV panels. It was found that, without cleaning, soiling reduced the output power by 43% after six months of exposure to an average ambient dust density of 0.7 mg/m3. The power and economic loss that would result from this power reduction for Qatar’s ongoing solar PV projects has also been estimated. For example, for the Al-Kharasaah project power plant, similar soiling loss would result in about a 10% power decrease after six months for typical ranges of dust density in Qatar’s environment; this, in turn, would result in an 11,000 QAR/h financial loss. This would pose a pressing need to mitigate soiling effects in PV power plants.

Download Full-text

Semantics of Voids within Data: Ignorance-Aware Machine Learning

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10040246 ◽

2021 ◽

Vol 10 (4) ◽

pp. 246

Author(s):

Vagan Terziyan ◽

Anton Nikulin

Keyword(s):

Spatial Data ◽

Information Science ◽

Nearest Neighbor ◽

Geographical Information ◽

Two Dimensional ◽

Prototype Selection ◽

Data Space ◽

Important Concern ◽

Traditional Classification ◽

Nearest Neighbor Classifiers

Operating with ignorance is an important concern of geographical information science when the objective is to discover knowledge from the imperfect spatial data. Data mining (driven by knowledge discovery tools) is about processing available (observed, known, and understood) samples of data aiming to build a model (e.g., a classifier) to handle data samples that are not yet observed, known, or understood. These tools traditionally take semantically labeled samples of the available data (known facts) as an input for learning. We want to challenge the indispensability of this approach, and we suggest considering the things the other way around. What if the task would be as follows: how to build a model based on the semantics of our ignorance, i.e., by processing the shape of “voids” within the available data space? Can we improve traditional classification by also modeling the ignorance? In this paper, we provide some algorithms for the discovery and visualization of the ignorance zones in two-dimensional data spaces and design two ignorance-aware smart prototype selection techniques (incremental and adversarial) to improve the performance of the nearest neighbor classifiers. We present experiments with artificial and real datasets to test the concept of the usefulness of ignorance semantics discovery.

Download Full-text

A Geospatial database of gastric cancer patients and associated potential risk factors including lifestyle and air pollution

BMC Research Notes ◽

10.1186/s13104-021-05506-x ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Fatemeh Hashemi Amin ◽

Mahtab Ghaemi ◽

Sayyed Mostafa Mostafavi ◽

Ladan Goshayeshi ◽

Khadijeh Rezaei ◽

...

Keyword(s):

Risk Factors ◽

Gastric Cancer ◽

Cancer Patients ◽

Spatial Data ◽

Geographical Information Systems ◽

Dietary Habits ◽

Age Groups ◽

Geographical Location ◽

Geographical Information ◽

Point Feature

Abstract Objectives Gastric cancer (GC) is a multifactorial disease and the fifth most frequent diagnosed cancer worldwide. It accounts for one third of cancer-related mortalities. Geospatial analysis using geographical information systems (GIS) can provide an efficient solution to identify spatial disparities associated with GC. As such, GIS enables policymakers to control cancer in a better way and identify the regions where interventions are needed. This study aims to publish a comprehensive dataset, which was applied to conduct a spatial analysis of GC patients in the city of Mashhad, Iran. Data description We provide a personal geodatabase, a Microsoft Access database that can store, query, and manage both spatial and non-spatial data, which contains four feature classes. “Male_Stomach_Cancer_Patients” and “Female_Stomach_Cancer_Patients” are point feature classes, which show the age and geographical location of 1156 GC cancer patients diagnosed between 2014 and 2017. “Air_Polution_Mashhad” is another point feature class that reveals the amount of six air pollutants, which was taken from Mashhad Environmental Pollutants Monitoring Center between 2017 and 2018. Finally, “Stomach_Cancer_and_Risk_Factors” is a polygon feature class of neighborhood division of Mashhad, consisting of contributor risk factors including dietary habits, smoking, alcohol use, body mass index and population by age groups for all 165 city neighborhoods.

Download Full-text

A Geographical Information System (GIS) study of Triassic vertebrate biochronology

Geological Magazine ◽

10.1017/s001675680500083x ◽

2005 ◽

Vol 142 (4) ◽

pp. 327-354 ◽

Cited By ~ 35

Author(s):

E. J. RAYFIELD ◽

P. M. BARRETT ◽

R. A. McDONNELL ◽

K. J. WILLIS

Keyword(s):

Spatial Data ◽

Geographical Information Systems ◽

Western Europe ◽

Temporal Distribution ◽

Geographical Information ◽

Late Triassic ◽

Spatial And Temporal Scales ◽

Potential Index ◽

The Stability ◽

Land Vertebrate

Geographical Information Systems (GIS) have been applied extensively to analyse spatial data relating to varied environmental issues, but have not so far been used to address biostratigraphical or macroevolutionary questions over extended spatial and temporal scales. Here, we use GIS techniques to test the stability, validity and utility of proposed Middle and Late Triassic ‘Land Vertebrate Faunachrons’ (LVFs), a global biostratigraphical framework based upon terrestrial/freshwater tetrapod occurrences. A database of tetrapod and megafloral localities was constructed for North America and Western Europe that also incorporated information on relevant palaeoenvironmental variables. This database was subjected to various spatial analysis techniques. Our GIS analysis found support at a global level for Eocyclotosaurus as an Anisian index taxon and probably Aetosaurus as a Norian indicator. Other tetrapod taxa are useful biostratigraphical/biochronological markers on a regional basis, such as Longosuchus and Doswellia for Late Carnian time. Other potential index fossils are hampered, however, by taxonomic instability (Mastodonsaurus, Metoposaurus, Typothorax, Paleorhinus, Pseudopalatus, Redondasaurus, Redondasuchus) and/or are not clearly restricted in temporal distribution (Paleorhinus, Angistorhinus, Stagonolepis, Metoposaurus and Rutiodon). This leads to instability in LVF diagnosis. We found only in the western Northern Hemisphere is there some evidence for an Anisian–Ladinian biochronological unit amalgamating the Perovkan and Berdyankian LVFs, and a possible late Carnian unit integrating the Otischalkian and Adamanian.Megaplants are generally not useful for biostratigraphical correlation in the Middle and Upper Triassic of the study area, but there is some evidence for a Carnian-age floral assemblage that corresponds to the combined Otischalkian and Adamanian LVFs. Environmental biases do not appear to strongly affect the spatial distribution of either the tetrapods or megaplants that have been proposed as index taxa in biostratigraphical schemes, though several examples of apparent environmental bias were detected by the analysis. Consequently, we argue that further revision and refinement of Middle and Late Triassic LVFs is needed before they can be used to support global or multi-regional biostratigraphical correlations. Caution should therefore be exercised when using the current scheme as a platform for macroevolutionary or palaeoecological hypotheses. Finally, this study demonstrates the potential of GIS as a powerful tool for tackling palaeontological questions over extended timescales.

Download Full-text

Research on Generation Rules of Chest Width Point Curve Based on 3D Female Mannequin

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.796.513 ◽

2013 ◽

Vol 796 ◽

pp. 513-518

Author(s):

Rong Jin ◽

Bing Fei Gu ◽

Guo Lian Liu

Keyword(s):

Point Cloud ◽

Three Dimensional ◽

Upper Body ◽

Secondary Development ◽

Linear Regression Method ◽

Point Cloud Data ◽

Body Type ◽

Cloud Data ◽

Engineering Software ◽

The Individual

In this paper 110 female undergraduates in Soochow University are measured by using 3D non-contact measurement system and manual measurement. 3D point cloud data of human body is taken as research objects by using anti-engineering software, and secondary development of point cloud data is done on the basis of optimizing point cloud data. In accordance with the definition of the human chest width points and other feature points, and in the operability of the three-dimensional point cloud data, the width, thickness, and length dimensions of the curve through the chest width point are measured. Classification of body type is done by choosing the ratio values as classification index which is the ratio between thickness and width of the curve. The generation rules of the chest curve are determined for each type by using linear regression method. Human arm model could be established by the computer automatically. Thereby the individual model of the female upper body mannequin modeling can be improved effectively.

Download Full-text

Mapping, cluster detection and evaluation of risk factors of ovine toxoplasmosis in Southern Italy

Geospatial health ◽

10.4081/gh.2016.432 ◽

2016 ◽

Vol 11 (2) ◽

Cited By ~ 5

Author(s):

Roberto Condoleo ◽

Vincenzo Musella ◽

Maria Paola Maurelli ◽

Antonio Bosco ◽

Giuseppe Cringoli ◽

...

Keyword(s):

Risk Factors ◽

Geographical Information Systems ◽

Southern Italy ◽

Geographical Information ◽

Economic Losses ◽

Common Source ◽

High Exposure ◽

Campania Region ◽

Source Of Infection ◽

Serological Data

Toxoplasmosis, an important cause of reproductive failure in sheep, is responsible for significant economic losses to the ovine industry worldwide. Moreover, ovine meat contaminated by the parasite Toxoplasma gondii is considered as a common source of infection for humans. The aim of this study was to develop point and risk profiling maps of T. gondii seroprevalence in sheep bred in Campania Region (Southern Italy) and analyse risk factors associated at the flock-level. We used serological data from a previous survey of 117 sheep flocks, while environmental and farm management information were obtained from an analysis based on geographical information systems and a questionnaire purveyance, respectively. An univariate Poisson regression model revealed that the type of farm production (milk and meat vs only meat) was the only independent variable associated with T. gondii positivity (P<0.02); the higher within-flock seroprevalence in milking herds suggests that milking practices might influence the spread of the infection on the farm. Neither environmental nor other management variables were significant. Since a majority of flocks were seasonally or permanently on pasture, the animals have a high exposure to infectious T. gondii oocysts, so the high within-flock seroprevalence might derive from this management factor. However, further studies are needed to better assess the actual epidemiological situation of toxoplasmosis in sheep and to clarify the factors that influence its presence and distribution.

Download Full-text

Optimal Site Selection for Sittinga Solar Park using Multi-Criteria Decision Analysis and Geographical Information Systems (GIS)

10.5194/gi-2016-4 ◽

2016 ◽

Cited By ~ 3

Author(s):

Andreas Georgiou ◽

Dimitrios Skarlatos

Keyword(s):

Spatial Data ◽

Geographical Information Systems ◽

Power Plants ◽

Solar Power ◽

Geographical Information ◽

Optimal Placement ◽

Equal Weight ◽

Analysis Tool ◽

Study Region ◽

Weighting Schemes

Abstract. Among the renewable powers sources, solar is rapidly becoming popular being inexhaustible, clean, and dependable. It is also becoming more efficient since the photovoltaic solar cells' power conversion efficiency is rising. Following these trends, solar power will become more affordable in years to come and considerable investments are to be expected. Despite the size of solar plants, the sitting procedure is a crucial factor for their efficiency and financial viability. Many aspects rule such decision; legal, environmental, technical, and financial to name some. This paper describes a general integrated framework to evaluate land suitability for the optimal placement of photovoltaic solar power plants, which is based on a combination of a Geographic Information System (GIS), remote sensing techniques and multi-criteria decision making methods. An application of the proposed framework for Limassol District in Cyprus is further illustrated. The combination of GIS and multi-criteria methods, consist an excellent analysis tool that creates an extensive database of spatial and non spatial data that will be used to simplify problems, to solve and promote the use of multiple criteria. A set of environmental, economic, social and technical constrains based on recent Cypriot legislation, European's Union policies and experts' advices, identifies the potential sites for solar park installation. The pair-wise comparison method in the context of the analytic hierarchy process (AHP) is applied to estimate the criteria weights in order to establish their relative importance in site evaluation. In addition, four different methods to combine information layers and check their sensitivity were used. The first considered all the criteria as being equally important and assign them equal weight, while the others grouped the criteria and graded them according to their objective perceived importance. The overall suitability of the study region for sitting solar park is appraised through the summation rule. Strict application of the framework depicts 3.0 % of the study region scoring best suitability index for solar resource exploitation, hence minimizing risk of a potential investment. However, using different weighting schemes for criteria, suitable areas may reach up to 83 % of the study region. The suggested methodological framework applied can be easily utilized by potential investors and renewable energy developers, through a front end web based application with proper GUI for personalized weighting schemes.

Download Full-text

SISFLOR: A COMPUTATIONAL SYSTEM TO DETERMINE THE OPTIMAL TREE BUCKING

Revista Engenharia na Agricultura - REVENG ◽

10.13083/reveng.v28i.3876 ◽

2020 ◽

Vol 28 ◽

pp. 192-201

Author(s):

Rodrigo Freitas Silva ◽

Marcelo Otone Aguiar ◽

Mayra Luiza Marques Da Silva ◽

Gilson Fernandes Da Silva ◽

Adriano Ribeiro De Mendonça

Keyword(s):

Optimization Technique ◽

Wood Products ◽

Tree Level ◽

Economic Losses ◽

Computational System ◽

Individual Tree ◽

Optimal Tree ◽

Cutting Pattern ◽

Forest Enterprises ◽

The Individual

A continuously competitive forest market and tied to the demands for wood products promotes the study and development of applications that increase the revenue of the forest enterprises. At harvesting, the cutting pattern (forest assortment) in which the trees are traced is traditionally determined by the experience of the chainsaw operator without using any optimization technique, which may result in economic losses in relation to the commercialized products. In general, there are numerous distinct assortments that can be chosen and hardly processed by a brute-force algorithm. This is the forest assortment problem at the individual tree level with the objetice of maximizing the commercial values of the felled trees. stem-level bucking optimization problem. The aim is to maximize the sales value of harvested trees. Dynamic Programming (DP) is an efficient optimization technique to determine the optimum bucking tree as it significantly reduces the number of calculations to be made. Thus, the objective of this work was to develop a modern and intuitive computational system that is able to find the optimum tree stem bucking through DP to help companies over the bole tracing, therefore, characterizing itself as a tool that supports decision making. After the execution of the system, the optimum assortment is shown by sequentially detailing all products that should be removed from the analyzed bole as well as their respective volumes and revenue.

Download Full-text