Vulnerability Assessment System Research Articles

Using National Household Travel Survey Data for the Assessment of Transportation System Vulnerabilities

This paper describes how the National Household Travel Survey (NHTS) data can be used within the framework of the Threat and Hazard Identification and Risk Assessment, recently promulgated by the Federal Emergency Management Agency, to assess transportation system vulnerability. The purpose of this paper is threefold: identify common ground between the Department of Homeland Security and the Department of Transportation in methods of risk assessment; look specifically at transportation vulnerabilities by shifting the focus from transportation system infrastructure to the travel patterns and attributes of individuals; and with data from Honolulu, Hawaii, evaluate the vulnerability of transportation assets. The proposed transportation system vulnerability assessment method uses data from the 2001 NHTS Oahu, Hawaii, add-on survey and probable coastal inundation scenario for events caused by hurricane storm surge, inland flooding, and sea level rise. A geographic information system approach is used to simulate travel routes and assess the transportation system vulnerabilities for individuals and households. The NHTS data contain vital information regarding home, work, and other travel locations. The data also contain travel mode information that can be used to produce quantitative indices for exposure and vulnerability, which can be overlaid with plausible hazard and threat scenarios. Recommendations are made for improving the quality, availability, and usefulness of NHTS data for the reduction of risks. The findings are relevant to transportation research on risk assessment and threat and hazard reduction.

Modeling stakeholder‐defined climate risk on the Upper Great Lakes

Climate change is believed to pose potential risks to the stakeholders of the Great Lakes due to changes in lake levels. This paper presents a model of stakeholder‐defined risk as a function of climate change. It describes the development of a statistical model that links water resources system performance and climate changes developed for the Great Lakes of North America. The function is used in a process that links bottom‐up water system vulnerability assessment to top‐down climate change information. Vulnerabilities are defined based on input from stakeholders and resource experts and are used to determine system performance thresholds. These thresholds are used to measure performance over a wide range of climate changes mined from a large (55,590 year) stochastic data set. The performance and climate conditions are used to create a climate response function, a statistical model to predict lake performance based on climate statistics. This function facilitates exploration and analysis of performance over a wide range of climate conditions. It can also be used to estimate risk associated with change in climate mean and variability resulting from climate change. Problematic changes in climate can be identified and the probability of those conditions estimated using climate projections or other sources of climate information. The function can also be used to evaluate the robustness of a regulation plan and to compare performance of alternate plans. This paper demonstrates the utility of the climate response function as applied within the context of the International Upper Great Lakes Study.

Groundwater Vulnerability Assessment System Based on GIS

On the basis of GIS technique, with single factor layer data of spatial database and Overlay analysis method, the designing of groundwater vulnerability assessment model for DRASTIC、LSD、DRTA、DLCT has been proposed. Under VC ++6.0, with the application of MapGIS development kit, the groundwater vulnerability assessment model system has been developed. It can associate professionals for performance evaluation of groundwater vulnerability.

Research on Vulnerability Assessment System of Complicated Power System

The factors which lead to large-area power outages are more than ever, when the complexity of the expansion power system is increasing. Therefore, it is important to assess the vulnerability of the complicated power system. This paper first proposes an objective, practical and adaptive vulnerability assessment system of complicated power system by use of Analytic Hierarchy Process, which based on multi-factors analysis of large-area outages. This system can find out the vulnerability point of the power system，and take advantages of the government, who can over-all plan, correspond and organize the social resource to reduce the vulnerability of power system and prevent large-area power outages, and the power enterprises who can maintaining the stability of power system by technology. Finally, the paper gives an assessment example of provincial power system.

A Network Security Risk Assessment Method Based on Immunity Algorithm

Based on the research of domestic and foreign vulnerability assessment systems, in this paper, we propose an improved network security assessment method based on Immunity algorithm. It integrates the advantages of both host based and network based scan system. Our goal is to explore the known security vulnerabilities, and to check hosts’ security effectively as well. It has the features of self-adaptive, distributed, and real time. Therefore, it provides a good solution to risk assessment for network security.

Assessment of vegetation vulnerability to ENSO warm events over Africa

The study investigates the vulnerability of vegetation over Africa to El-Niño Southern Oscillation (ENSO) events using the moving window statistical correlation analysis technique. The correlation analysis was done between Normalized Difference Vegetation Index (NDVI) data from Advanced Very High Resolution Radiometer (AVHRR) and an ENSO index, namely Multivariate ENSO Index (MEI). The study develops a new monitoring approach (ENSO vulnerability assessment system) to quantify the relationships between monthly maximum NDVI anomalies and their month-to-month correlations with the ENSO indices for the vegetative land areas of Africa. The data used in this study was for the period 1982–2006. The new monitoring approach was used in the assessment of the long-time vegetation sensitivity to the ENSO warm events that occurred during the study period. A map of Africa indicating the vegetation vulnerability to ENSO is produced. Different areas of vegetation vulnerability are identified within the main vegetation cover classes. For the African vegetative land, 16% of the total area was characterized by moderate vulnerability of vegetation to El-Niño, whereas 1.18% showed high vulnerability. Results suggest that the vulnerability of vegetative land surfaces across Africa to climate extremes, such as ENSO depends considerably on the vegetation type. In particular, results show that areas of equatorial rainforest are more resistant to drought stress than the wooded and non-wooded vegetation categories.

Vulnerability Assessment of a Large Sized Power System Using Neural Network Considering Various Feature Extraction Methods

Vulnerability assessment of power systems is important so as to determine their ability to continue to provide service in case of any unforeseen catastrophic contingency such as power system component failures, communication system failures, human operator error, and natural calamity. An approach towards the development of on-line power system vulnerability assessment is by means of using an artificial neural network (ANN), which is being used successfully in many areas of power systems because of its ability to handle the fusion of multiple sources of data and information. An important consideration when applying ANN in power system vulnerability assessment is the proper selection and dimension reduction of training features. This paper aims to investigate the effect of using various feature extraction methods on the performance of ANN as well as to evaluate and compare the efficiency of the proposed feature extraction method named as neural network weight extraction. For assessing vulnerability of power systems, a vulnerability index based on power system loss is used and considered as the ANN output. To illustrate the effectiveness of ANN considering various feature extraction methods for vulnerability assessment on a large sized power system, it is verified on the IEEE 300-bus test system.

An OVAL-based active vulnerability assessment system for enterprise computer networks

Many security problems are caused by vulnerabilities hidden in enterprise computer networks. It is very important for system administrators to have knowledge about the security vulnerabilities. How...

Continuous monitoring of residual chlorine concentrations in response to controlled microbial intrusions in a laboratory-scale distribution system

The objective of this work was to evaluate the efficacy of deploying free chlorine sensors as surrogate monitors for bacterial contamination events in drinking water distribution systems. An on-line sensor integral with a laboratory-scale distribution system (LDS) was shown to respond rapidly to changes in residual free chlorine concentrations induced by injected loads of Escherichia coli suspended in a chlorine demand free buffer. The magnitude of the residual response was proportional to the injected cell concentration, the background free chlorine concentration in the LDS, and the contact time between the chlorine residual and the injected suspension, consistent with previous results in batch reactors. The magnitude of the residual response was predicted when kinetic models developed from reaction kinetics between free chlorine and E. coli determined in batch systems were evaluated at contact times determined from LDS hydraulics. This result highlights the suitability of using batch kinetics when modeling contaminant-induced chlorine decay in the distribution system. Modeling the propagation of chlorine demand signals generated by specific pathogens could aid in the assessment of distribution system vulnerability.

Comparative Approaches for Assessing Network Vulnerability

A common theme in analysis and evaluation of network-based critical infrastructure is the assessment of system vulnerability. Graph theoretic, simulation, and optimization-based techniques have played a significant role in examining potential network vulnerabilities given the insights they can provide for mitigating facility loss and prioritizing fortification efforts. Central to these approaches is the concept of facility (arc—node) importance or criticality to system survivability. Assessments of network vulnerability can dramatically differ based on how facility importance is characterized. In this review, various approaches for assessing facility importance and network vulnerability are examined. The key differences in these approaches are the ways in which a facility's role in maintaining network operability is evaluated given arc—node disruption. Comparative results suggest significant differences exist among measures of facility importance and network performance. Furthermore, the subsequent incongruities in these measures and their implications need to be clearly understood to support interdiction risk and vulnerability assessment for critical infrastructures.

The strategic power infrastructure defense (SPID) system. A conceptual design

A power system can become vulnerable for various reasons, these sources of vulnerability are either internal or external to the infrastructure that comprises the power system. Threats from vulnerability sources that are internal to the civil infrastructure may be reduced by decreasing the probability and severity of occurrence through the improved engineering of related systems. On the other hand, threats from vulnerability sources that are external to the infrastructure may be reduced by decreasing the severity of occurrence. To prevent or reduce catastrophic failures and cascading sequences of events caused by the various sources of vulnerability, the Advanced Power Technologies Consortium is researching ways to revolutionize defense strategies and technologies that will significantly reduce the vulnerability of the power infrastructure. Our vision is a wide-area, intelligent, adaptive protection and control system that empowers future grids by providing critical and extensive information in real time, assessing system vulnerability quickly, and performing timely self-healing and adaptive reconfiguration actions based on system-wide analysis. The proposed system is referred to as the strategic power infrastructure defense (SPID) system. The article discusses the conceptual design of the SPID system and the associated technical challenges. The fundamentally important concept is that the SPID system provides self-healing and adaptive reconfiguration capabilities for power grids based on wide-area system vulnerability assessment. Some experts believe that if 0.4% of the load had been shed for 30 minutes, the widespread power outage in the Western United States on 10 August 1996 could have been avoided. The SPID is intended to identify such load-shedding actions in real time based on proposed vulnerability assessment and protection/control systems.

Application of artificial neural networks in power system security and vulnerability assessment

In a companion paper by A.A. Fouad et al. the concept of system vulnerability is introduced as a new framework for power system dynamic security assessment. Using the transient energy function (TEF) method of transient stability analysis, the energy margin /spl Delta/V is used as an indicator of the level of security, and its sensitivity to a changing system parameter p (/spl delta//spl Delta/V//spl delta/p) as an indicator of its trend with changing system conditions. These two indicators are combined to determine the degree of system vulnerability to contingent disturbances in a stability-limited power system. Thresholds for acceptable levels of the security indicator and its trend are related to the stability limits of a critical system parameter (plant generation limits). Operating practices and policies are used to determine these thresholds. In this paper the artificial neural networks (ANNs) technique is applied to the concept of system vulnerability within the recently developed framework, for fast pattern recognition and classification of system dynamic security status. A suitable topology for the neural network is developed, and the appropriate training method and input and output signals are selected. The procedure developed is successfully applied to the IEEE 50-generator test system. Data previously obtained by heuristic techniques are used for training the ANN. >

Land cover inventory in the Netherlands using remote sensing; application in a soil and groundwater vulnerability assessment system

Abstract Information on actual land cover is necessary for various applications, such as soil and groundwater protection studies and hydrological studies. Therefore, a decision to produce a national land cover data base of the Netherlands, using satellite images, was made. The first version of the data base is now available for the whole of the Netherlands. Prior to the supervised classification the area was stratified in more or less homogeneous areas. Because cost, time and logistics required for a random sampling of the entire country were prohibitive, a mixed quantitative/qualitative classification accuracy assessment procedure was proposed. Classification performances were quantitatively assessed by comparing the classification results with digitized ground reference maps using a Geographical Information System (GIS). This offers a flexible method for locating the incorrectly labelled pixels and determining the possible reasons thereof. Classification accuracy of the land cover types which do not cha...

A computer system for hurricane hazard assessment

Rapid economic development and population growth in coastal regions of the U.S. has resulted in an increase in vulnerability of population and property to the hurricane hazard. State and local emergency management officials need a detailed information base of potential loss estimates to prepare sound emergency preparedness plans. A computer system, known as the Vulnerability Assessment and Mapping System (VAMS), has been designed to estimate the extent and spatial distribution of potential casualties, and losses to housing and commercial-industrial facilities as a result of hurricane landfall. The VAMS system is flexible as users can choose to consider different types of hurricanes with varying surge and wind patterns and points of landfall for determining potential loss estimates within a given study area. The results of the analysis can be displayed in computer-generated grid map form or as summary statistics in tabular form. The VAMS system is then applied to coastal portions of the Houston-Galveston metropolitan area in the State of Texas. The VAMS system is part of a comprehensive hurricane impact study for the Texas Governor's Division of Emergency Management.