A Three-Part Bayesian Network for Modeling Dwelling Fires and Their Impact upon People and Property.

In the United Kingdom, dwelling fires are responsible for the majority of all fire-related fatalities. The development of these incidents involves the interaction of a multitude of variables that combine in many different ways. Consequently, assessment of dwelling fire risk can be complex, which often results in ambiguity during fire safety planning and decision making. In this article, a three-part Bayesian network model is proposed to study dwelling fires from ignition through to extinguishment in order to improve confidence in dwelling fire safety assessment. The model incorporates both hard and soft data, delivering posterior probabilities for selected outcomes. Case studies demonstrate how the model functions and provide evidence of its use for planning and accident investigation

A risk-based fire and rescue management system

This PhD focuses on developing a risk-based fire and rescue model for dwelling fires which importantly, is where most fire deaths occur each year. There are a vast number of variables to consider when modelling dwellings, for example variations will arise in terms of geographical location, fire safety arrangements, characteristics of occupants, activities of occupants, among others. As for the occurrence of fire itself, each incident will be unique in terms of time of day, type of fire, state of occupants, fire cues, etc. What all these variations signify is that the potential magnitude of the next fire event and its consequences are generally unpredictable. Because of complicated scenarios, unpredictability of outcomes, and high frequency of incidents, Fire and Rescue Serices (FRS) have to be both capable and flexible in operation; however finding the optimal way of providing emergency cover and minimizing risk is a complicated task which often results in reasoning and decisions taking place under uncertainty. In order to diminish some of this uncertainty and improve confidence in decision making, an extensive four-part Bayesian Network (BN) model is developed focusing on dwelling fires within the UK. The intention is to model the sequence of events which may occur during a fire from ignition through to extinguishment with the objective of assessing, under specified conditions, fire safety at a given location; this should assist in determining what the most important safety issues are for the purpose of improving fire prevention and mitigating consequences in order to reduce fire risk across residential communities. The model itself is broken down into four parts which can function independently or together as an integrated network. The model parts are as follows: Part I - "Initial fire development". Part II - "Occupancy response and further fire development". Part III - "Advanced fire situation and consequences". Part IV - "Fire response time module". Within the project a risk-based fire and rescue operations management framework is also presented to demonstrate how the BN model could fit into the strategic management of FRS's and how it could link up with other tools and data collection programmes. The BN model may prove to be useful for strategic decision making within FRS's

Exploration of the Bayesian Network framework for modelling window control behaviour

© 2017 Elsevier Ltd Extended literature reviews confirm that the accurate evaluation of energy-related occupant behaviour is a key factor for bridging the gap between predicted and actual energy performance of buildings. One of the key energy-related human behaviours is window control behaviour that has been modelled by different probabilistic modelling approaches. In recent years, Bayesian Networks (BNs) have become a popular representation based on graphical models for modelling stochastic processes with consideration of uncertainty in various fields, from computational biology to complex engineering problems. This study investigates the potential applicability of BNs to capture underlying complicated relationships between various influencing factors and energy-related behavioural actions of occupants in buildings: in particular, window opening/closing behaviour of occupants in residential buildings is investigated. This study addresses five key research questions related to modelling window control behaviour: (A) variable selection for identifying key drivers impacting window control behaviour, (B) correlations between key variables for structuring a statistical model, (C) target definition for finding the most suitable target variable, (D) BN model with capabilities to treat mixed data, and (E) validation of a stochastic BN model. A case study on the basis of measured data in one residential apartment located in Copenhagen, Denmark provides key findings associated with the five research questions through the modelling process of developing the BN model

Impact of New Madrid Seismic Zone Earthquakes on the Central USA, Vol. 1 and 2

The information presented in this report has been developed to support the Catastrophic Earthquake Planning Scenario workshops held by the Federal Emergency Management Agency. Four FEMA Regions (Regions IV, V, VI and VII) were involved in the New Madrid Seismic Zone (NMSZ) scenario workshops. The four FEMA Regions include eight states, namely Illinois, Indiana, Kentucky, Tennessee, Alabama, Mississippi, Arkansas and Missouri. The earthquake impact assessment presented hereafter employs an analysis methodology comprising three major components: hazard, inventory and fragility (or vulnerability). The hazard characterizes not only the shaking of the ground but also the consequential transient and permanent deformation of the ground due to strong ground shaking as well as fire and flooding. The inventory comprises all assets in a specific region, including the built environment and population data. Fragility or vulnerability functions relate the severity of shaking to the likelihood of reaching or exceeding damage states (light, moderate, extensive and near-collapse, for example). Social impact models are also included and employ physical infrastructure damage results to estimate the effects on exposed communities. Whereas the modeling software packages used (HAZUS MR3; FEMA, 2008; and MAEviz, Mid-America Earthquake Center, 2008) provide default values for all of the above, most of these default values were replaced by components of traceable provenance and higher reliability than the default data, as described below. The hazard employed in this investigation includes ground shaking for a single scenario event representing the rupture of all three New Madrid fault segments. The NMSZ consists of three fault segments: the northeast segment, the reelfoot thrust or central segment, and the southwest segment. Each segment is assumed to generate a deterministic magnitude 7.7 (Mw7.7) earthquake caused by a rupture over the entire length of the segment. US Geological Survey (USGS) approved the employed magnitude and hazard approach. The combined rupture of all three segments simultaneously is designed to approximate the sequential rupture of all three segments over time. The magnitude of Mw7.7 is retained for the combined rupture. Full liquefaction susceptibility maps for the entire region have been developed and are used in this study. Inventory is enhanced through the use of the Homeland Security Infrastructure Program (HSIP) 2007 and 2008 Gold Datasets (NGA Office of America, 2007). These datasets contain various types of critical infrastructure that are key inventory components for earthquake impact assessment. Transportation and utility facility inventories are improved while regional natural gas and oil pipelines are added to the inventory, alongside high potential loss facility inventories. The National Bridge Inventory (NBI, 2008) and other state and independent data sources are utilized to improve the inventory. New fragility functions derived by the MAE Center are employed in this study for both buildings and bridges providing more regionally-applicable estimations of damage for these infrastructure components. Default fragility values are used to determine damage likelihoods for all other infrastructure components. The study reports new analysis using MAE Center-developed transportation network flow models that estimate changes in traffic flow and travel time due to earthquake damage. Utility network modeling was also undertaken to provide damage estimates for facilities and pipelines. An approximate flood risk model was assembled to identify areas that are likely to be flooded as a result of dam or levee failure. Social vulnerability identifies portions of the eight-state study region that are especially vulnerable due to various factors such as age, income, disability, and language proficiency. Social impact models include estimates of displaced and shelter-seeking populations as well as commodities and medical requirements. Lastly, search and rescue requirements quantify the number of teams and personnel required to clear debris and search for trapped victims. The results indicate that Tennessee, Arkansas, and Missouri are most severely impacted. Illinois and Kentucky are also impacted, though not as severely as the previous three states. Nearly 715,000 buildings are damaged in the eight-state study region. About 42,000 search and rescue personnel working in 1,500 teams are required to respond to the earthquakes. Damage to critical infrastructure (essential facilities, transportation and utility lifelines) is substantial in the 140 impacted counties near the rupture zone, including 3,500 damaged bridges and nearly 425,000 breaks and leaks to both local and interstate pipelines. Approximately 2.6 million households are without power after the earthquake. Nearly 86,000 injuries and fatalities result from damage to infrastructure. Nearly 130 hospitals are damaged and most are located in the impacted counties near the rupture zone. There is extensive damage and substantial travel delays in both Memphis, Tennessee, and St. Louis, Missouri, thus hampering search and rescue as well as evacuation. Moreover roughly 15 major bridges are unusable. Three days after the earthquake, 7.2 million people are still displaced and 2 million people seek temporary shelter. Direct economic losses for the eight states total nearly $300 billion, while indirect losses may be at least twice this amount. The contents of this report provide the various assumptions used to arrive at the impact estimates, detailed background on the above quantitative consequences, and a breakdown of the figures per sector at the FEMA region and state levels. The information is presented in a manner suitable for personnel and agencies responsible for establishing response plans based on likely impacts of plausible earthquakes in the central USA.Armu W0132T-06-02unpublishednot peer reviewe

Bayesian network modelling of an offshore electrical generation system for applications within an asset integrity case for normally unattended offshore installations

This article proposes the initial stages of the application of Bayesian networks in conducting quantitative risk assessment of the integrity of an offshore system. The main focus is the construction of a Bayesian network model that demonstrates the interactions of multiple offshore safety critical elements to analyse asset integrity. The majority of the data required to complete the Bayesian network was gathered from various databases and past risk assessment experiments and projects. However, where data were incomplete or non-existent, expert judgement was applied through pairwise comparison, analytical hierarchy process and a symmetric method to fill these data gaps and to complete larger conditional probability tables. A normally unattended installation–Integrity Case will enable the user to determine the impact of deficiencies in asset integrity and demonstrate that integrity is being managed to ensure safe operations in situations whereby physical human-to-machine interaction is not occurring. The Integrity Case can be said to be dynamic as it shall be continually updated for an installation as the quantitative risk analysis data are recorded. This allows for the integrity of the various systems and components of an offshore installation to be continually monitored. The Bayesian network allows cause and effect relationships to be modelled through clear graphical representation. The model accommodates for continual updating of failure data

Emergency logistics for wildfire suppression based on forecasted disaster evolution

This paper aims to develop a two-layer emergency logistics system with a single depot and multiple demand sites for wildfire suppression and disaster relief. For the first layer, a fire propagation model is first built using both the flame-igniting attributes of wildfires and the factors affecting wildfire propagation and patterns. Second, based on the forecasted propagation behavior, the emergency levels of fire sites in terms of demand on suppression resources are evaluated and prioritized. For the second layer, considering the prioritized fire sites, the corresponding resource allocation problem and vehicle routing problem (VRP) are investigated and addressed. The former is approached using a model that can minimize the total forest loss (from multiple sites) and suppression costs incurred accordingly. This model is constructed and solved using principles of calculus. To address the latter, a multi-objective VRP model is developed to minimize both the travel time and cost of the resource delivery vehicles. A heuristic algorithm is designed to provide the associated solutions of the VRP model. As a result, this paper provides useful insights into effective wildfire suppression by rationalizing resources regarding different fire propagation rates. The supporting models can also be generalized and tailored to tackle logistics resource optimization issues in dynamic operational environments, particularly those sharing the same feature of single supply and multiple demands in logistics planning and operations (e.g., allocation of ambulances and police forces). © 2017 The Author(s

Data Assimilation for Agent-Based Simulation of Smart Environment

Agent-based simulation of smart environment finds its application in studying people’s movement to help the design of a variety of applications such as energy utilization, HAVC control and egress strategy in emergency situation. Traditionally, agent-based simulation is not dynamic data driven, they run offline and do not assimilate real sensor data about the environment. As more and more buildings are equipped with various sensors, it is possible to utilize real time sensor data to inform the simulation. To incorporate the real sensor data into the simulation, we introduce the method of data assimilation. The goal of data assimilation is to provide inference about system state based on the incomplete, ambiguous and uncertain sensor data using a computer model. A typical data assimilation framework consists of a computer model, a series of sensors and a melding scheme. The purpose of this dissertation is to develop a data assimilation framework for agent-based simulation of smart environment. With the developed data assimilation framework, we demonstrate an application of building occupancy estimation which focuses on position estimation using the framework. We build an agent based model to simulate the occupants’ movement s in the building and use this model in the data assimilation framework. The melding scheme we use to incorporate sensor data into the built model is particle filter algorithm. It is a set of statistical method aiming at compute the posterior distribution of the underlying system using a set of samples. It has the benefit that it does not have any assumption about the target distribution and does not require the target system to be written in analytic form .To overcome the high dimensional state space problem as the number of agents increases, we develop a new resampling method named as the component set resampling and evaluate its effectiveness in data assimilation. We also developed a graph-based model for simulating building occupancy. The developed model will be used for carrying out building occupancy estimation with extremely large number of agents in the future

Development of an Algorithm to Convert Linear Belief Function inputs to Exponential Conditional Probability Functions for Multiple Method Applications

Evidential Reasoning (ER), based on the Dempster-Schafer theory of evidence, and Bayesian Networks (BN) are two distinct theories and methodologies for modelling and reasoning with data regarding propositions in uncertain domains. Both ER and BNs incorporate graphical representations and quantitative approaches of uncertainty. BNs are probability models consisting of a directed acyclic graph, which represents conditional independence assumptions in the joint probability distribution. Whereas ER graphically describes knowledge through an evaluation hierarchy and the relationships of the attributes based on Dempster-Shafer theory of belief functions. Therefore, this paper proposes an algorithm, which allows for the conversion of the linear input data of ER (belief degrees and relative weights) to the exponential data input of BNs (conditional probability tables (CPTs)). The algorithm is applied to a validated case study where the ER approach has been utilized for decision-makin

Modelling spatial temporal patterns and drivers of urban residential fire risk

Fire risk, in general, is the probability of a fire occurrence and its potential consequences (e.g. injuries/deaths or financial losses). An exposure to the source of fire ignition such as a live flame or a spark that is further fuelled by the presence of combustible materials, faulty electrical wiring or cooking devices, directly contributes to fire risk. It also hinges on an individual’s perception of fire risk, exhibited in situ behaviour such as alcohol drinking habits and the preparedness to respond to threat from fire. More broadly, fire risk is influenced by the size and characteristics of the population at risk or exposed to a fire hazard, and the levels of community resilience, which reflect the sustained ability to utilize available resources to respond to, withstand, and recover from adverse situations. Fire risk, therefore, is difficult to examine as it is driven by a multitude of interwoven factors. There are numerous studies that have applied a range of methods to model fire risk. These methods undoubtedly provide a useful baseline to enhance emergency response and improve resource allocation. Nonetheless, the accuracy, reliability and robustness of these models can be further improved by considering fire risk as a stochastic phenomenon. Furthermore, the role, which space and time plays in shaping fire risk a stochastic process is often overlooked. The aim of this study was to develop stochastic models to estimate the likelihood of residential fire occurrence and to identify key urban characteristics underpinning fire patterns over time and across space. This study addressed four interrelated key research questions: (i) How does a residential fire pattern occur over time and space? (ii) Can the probability of a residential fire occurrence be predicted as a stochastic process? (iii) How do the urban characteristics impact on residential fire risk? (iv) What spatially-integrated strategies can be developed to mitigate fire risk in an urban setting? The application of the Markov chain model and Geographically Weighted Regression (GWR) is a key contribution of this thesis because of the novelty of the methodology in quantifying residential fire risk which not only potentially improves the accuracy and reliability of fire risk modelling, but also enriches our understanding of behaviour associated with fire risk in relation to space, time, and situated context at the local level. These models are constructed using residential fire data for the Melbourne Australia, spanning a ten-year period from June 2005 to May 2015. The key findings demonstrate that, first, the incidence of residential fires across Melbourne during a 10-year period show a higher degree of fluctuation with a strong seasonal variation according to the months of the year. June-August is recorded as having the highest rate whereas March-April and November recorded the lowest rate of fire occurrence. Second, the mapping of the probability of fire occurrence across the Melbourne metropolis shows a city-centric spatial pattern where inner-city sub-regions are relatively more vulnerable to fire than are the outer sub-regions. Third, the time threshold that affects the fire risk levels within a neighbourhood that has had a fire is about two months. When this period of low fire risk elapses, the probability of a fire increases to the normal baseline, equivalent to that in areas with no fire. Fourth, a fire that occurs in a distant area has no significant effect on mitigating fire risk within the neighbourhood. When a distance threshold of 5 km is reached, either one fire or no fire in the past significantly increases the probability of fire. Areas with two or more fires within the neighbourhood are likely to reduce the chance of a fire in the initial period. Fifth, the key findings also reveal that the distribution of residential fires across Melbourne has a complex pattern and is associated with both temporally and spatially-varying neighbourhood attributes. The effect of socio-spatial characteristics such as language, residential mobility, home ownership, type of dwellings, and dwelling density, in relation to residential fires risk tends to be inconsistent across urban areas. Different areas have different contextual situations which influence the level of residential fire risk. These findings provide new empirical evidence useful for fire agencies seeking to establish appropriate strategies to mitigate adverse impacts of fire on communities. It can also help to identify high fire risk areas and to geo-target when and where to disseminate fire safety information to increase residents’ awareness of fire risk