Development and evaluation of multiple criteria decision-making approaches to watershed management

Decision-making in environmental management is complex due to the multiplicity arid diversity of management objectives and technological choices. This suggests that modelers and experts could utilize (I) multiple-criteria decision-making (MCDM) approaches to assist stakeholder groups in integrating and synthesizing relevant data and information to address ecological and socio-economic concerns and (2) uncertainty approaches to quantify the risks related to the impact of decision alternatives. Since decisions made under uncertainty and MCDM methods have been studied almost independently, most of the MCDM approaches do not address the uncertainties of real world decision situations. This dissertation presents the use of a MCDM methodology and its related decision-making tool, RESTORE. RESTORE is an integrative geographical information system-based decision-making tool that was developed to help watershed councils prioritize and evaluate restoration activities at the watershed level. RESTORE's deterministic performance evaluation module is developed from experts' knowledge and experiences. However, to filly address the complexity of the various landscape processes and human subjectivity, RESTORE should involve uncertainties inherent to experts' knowledge. No single method is able to model all types of uncertainty, therefore the examination of various uncertainty theories is critical before selecting one best suited to a specific decision context. This work explores three uncertainty theories: certainty factor model, Dempster-Shafer theory, and fuzzy set theory. To evaluate these methods in a MCDM watershed restoration context, we (1) identified criteria to assess the suitability of a method for a specific MCDM context, (2) characterized each theory in terms of the identified criteria using RESTORE, and (3) applied each theory using RESTORE. Special emphasis was given to the development of a comprehensive fuzzy MCDM methodology. Uncertainty-based MCDM approaches provide a valuable tool in analyzing complex watershed management issues. When used properly, the proposed MCDM methodology allows decision-makers (DMs) to explore a broader range of drivers and consequences. The inclusion of uncertainty analysis provides DMs with meaningful information on the quality of the evidence supporting the impact of a decision alternative, allowing them to make more informed decisions

Recommendation Framework Based on Subjective Logic in Decision Support Systems

In this thesis our goals are to investigate the suitability of subjective logic within the decision support context that requires connectivity to complex data, user specification of frames of discernment, representation of complex reasoning expressions, an architecture that supports distributed usage of a decision support tool based on a client-server approach that separates user interactions on the browser side from computational engines for calculations on the server side, and analysis of the suitability and limitations of the proposed architecture

Uncertainty Management of Intelligent Feature Selection in Wireless Sensor Networks

Wireless sensor networks (WSN) are envisioned to revolutionize the paradigm of monitoring complex real-world systems at a very high resolution. However, the deployment of a large number of unattended sensor nodes in hostile environments, frequent changes of environment dynamics, and severe resource constraints pose uncertainties and limit the potential use of WSN in complex real-world applications. Although uncertainty management in Artificial Intelligence (AI) is well developed and well investigated, its implications in wireless sensor environments are inadequately addressed. This dissertation addresses uncertainty management issues of spatio-temporal patterns generated from sensor data. It provides a framework for characterizing spatio-temporal pattern in WSN. Using rough set theory and temporal reasoning a novel formalism has been developed to characterize and quantify the uncertainties in predicting spatio-temporal patterns from sensor data. This research also uncovers the trade-off among the uncertainty measures, which can be used to develop a multi-objective optimization model for real-time decision making in sensor data aggregation and samplin

A framework for managing global risk factors affecting construction cost performance

Poor cost performance of construction projects has been a major concern for both contractors and clients. The effective management of risk is thus critical to the success of any construction project and the importance of risk management has grown as projects have become more complex and competition has increased. Contractors have traditionally used financial mark-ups to cover the risk associated with construction projects but as competition increases and margins have become tighter they can no longer rely on this strategy and must improve their ability to manage risk. Furthermore, the construction industry has witnessed significant changes particularly in procurement methods with clients allocating greater risks to contractors. Evidence shows that there is a gap between existing risk management techniques and tools, mainly built on normative statistical decision theory, and their practical application by construction contractors. The main reason behind the lack of use is that risk decision making within construction organisations is heavily based upon experience, intuition and judgement and not on mathematical models. This thesis presents a model for managing global risk factors affecting construction cost performance of construction projects. The model has been developed using behavioural decision approach, fuzzy logic technology, and Artificial Intelligence technology. The methodology adopted to conduct the research involved a thorough literature survey on risk management, informal and formal discussions with construction practitioners to assess the extent of the problem, a questionnaire survey to evaluate the importance of global risk factors and, finally, repertory grid interviews aimed at eliciting relevant knowledge. There are several approaches to categorising risks permeating construction projects. This research groups risks into three main categories, namely organisation-specific, global and Acts of God. It focuses on global risk factors because they are ill-defined, less understood by contractors and difficult to model, assess and manage although they have huge impact on cost performance. Generally, contractors, especially in developing countries, have insufficient experience and knowledge to manage them effectively. The research identified the following groups of global risk factors as having significant impact on cost performance: estimator related, project related, fraudulent practices related, competition related, construction related, economy related and political related factors. The model was tested for validity through a panel of validators (experts) and crosssectional cases studies, and the general conclusion was that it could provide valuable assistance in the management of global risk factors since it is effective, efficient, flexible and user-friendly. The findings stress the need to depart from traditional approaches and to explore new directions in order to equip contractors with effective risk management tools

Technologies for safe and resilient earthmoving operations: A systematic literature review

Resilience engineering relates to the ability of a system to anticipate, prepare, and respond to predicted and unpredicted disruptions. It necessitates the use of monitoring and object detection technologies to ensure system safety in excavation systems. Given the increased investment and speed of improvement in technologies, it is necessary to review the types of technology available and how they contribute to excavation system safety. A systematic literature review was conducted which identified and classified the existing monitoring and object detection technologies, and introduced essential enablers for reliable and effective monitoring and object detection systems including: 1) the application of multisensory and data fusion approaches, and 2) system-level application of technologies. This study also identified the developed functionalities for accident anticipation, prevention and response to safety hazards during excavation, as well as those that facilitate learning in the system. The existing research gaps and future direction of research have been discussed

A Risk-Based IoT Decision-Making Framework Based on Literature Review with Human Activity Recognition Case Studies

The Internet of Things (IoT) is a key and growing technology for many critical real-life applications, where it can be used to improve decision making. The existence of several sources of uncertainty in the IoT infrastructure, however, can lead decision makers into taking inappropriate actions. The present work focuses on proposing a risk-based IoT decision-making framework in order to effectively manage uncertainties in addition to integrating domain knowledge in the decision-making process. A structured literature review of the risks and sources of uncertainty in IoT decision-making systems is the basis for the development of the framework and Human Activity Recognition (HAR) case studies. More specifically, as one of the main targeted challenges, the potential sources of uncertainties in an IoT framework, at different levels of abstraction, are firstly reviewed and then summarized. The modules included in the framework are detailed, with the main focus given to a novel risk-based analytics module, where an ensemble-based data analytic approach, called Calibrated Random Forest (CRF), is proposed to extract useful information while quantifying and managing the uncertainty associated with predictions, by using confidence scores. Its output is subsequently integrated with domain knowledge-based action rules to perform decision making in a cost-sensitive and rational manner. The proposed CRF method is firstly evaluated and demonstrated on a HAR scenario in a Smart Home environment in case study I and is further evaluated and illustrated with a remote health monitoring scenario for a diabetes use case in case study II. The experimental results indicate that using the framework’s raw sensor data can be converted into meaningful actions despite several sources of uncertainty. The comparison of the proposed framework to existing approaches highlights the key metrics that make decision making more rational and transparent

Facilitating Brownfield Redevelopment Projects: Evaluation, Negotiation, and Policy

A risky project evaluation technique called the fuzzy real options analysis is developed to evaluate brownfield redevelopment projects. Other decision making techniques, such as multiple criteria analysis and conflict analysis, can be incorporated into fuzzy real options analysis to facilitate negotiations on brownfield redevelopment among decision makers (DMs). The value of managerial flexibility, which is important in negotiations and policy making for brownfield redevelopment, is overlooked when the traditional evaluation method, net present value (NPV), is employed. Findings of this thesis can be used to promote brownfield redevelopment, thereby helping to eliminate environmental threats and enhance regional sustainability. A brownfield is an abandoned or underutilized property that contains, or may contain, pollutants, hazardous substances, or contaminants from previous usage, typically industrial activity. Brownfields often occur when the local economy transits from industrial to service-oriented seeking more profit. Governments actively promote brownfield redevelopment to eliminate public health threats, help economic transition, and enhance sustainability. However, developers are reluctant to participate in brownfield redevelopment because they often regard these projects as unprofitable when using classic evaluation techniques. On the other hand, case studies show that brownfield redevelopment projects can be good business opportunities for developers. An improved evaluation method is developed in order to estimate the value of a brownfield more accurately. The main reason that makes the difference between estimates and ''actual'' values lies in the failure of the deterministic project evaluation tool to price the value of uncertainty, which leads to efforts to enhance the decision making under uncertainty. Real options modelling, which extends the ability of option pricing models in real asset evaluation, is employed in risky project evaluation because of its capacity to handle uncertainties. However, brownfield redevelopment projects contain uncertain factors that have no market price, thus violating the assumption of option pricing models for which all risks have been reflected in the market. This problem, called private risk, is addressed by incorporating fuzzy numbers into real options in this thesis, which can be called fuzzy real options. Fuzzy real options are shown to generalize the original model to deal with additional kinds of uncertainties, making them more suitable for project evaluation. A numerical technique based on hybrid variables is developed to price fuzzy real options. We proposed an extension of Least Squares Monte-Carlo simulation (LSM) that produces numerical evaluations of options. A major advantage of this methodology lies in its ability to produce results regardless of whether or not an analytic solution exists. Tests show that the generalized LSM produces similar results to the analytic valuation of fuzzy real options, when this is possible. To facilitate parameter estimation for the fuzzy real options model, another numerical method is proposed to represent the likelihood of contamination of a brownfield using fuzzy boundaries. Linguistic quantifiers and ordered weighted averaging (OWA) techniques are utilized to determine the likelihood of pollution at sample locations based on multiple environmental indicators, acting as a fuzzy deduction rule to calculate the triangle membership functions of the fuzzy parameters. Risk preferences of DMs are expressed as different ''ORness'' levels of OWA operators, which affect likelihood estimates. When the fuzzy boundaries of a brownfield are generated by interpolation of sample points, the parameters of fuzzy real options, drift rate and volatility, can be calculated as fuzzy numbers. Hence, this proposed method can act as an intermediary between DMs and the fuzzy real options models, making this model much easier to apply. The values of DMs to a brownfield can be input to the graph model for conflict resolution (GMCR) to identify possible resolutions during brownfield redevelopment negotiation among all possible states, or combinations of DMs' choices. Major redevelopment policies are studied using a brownfield redevelopment case, Ralgreen Community in Kitchener, Ontario, Canada. The fuzzy preference framework and probability-based comparison method to rank fuzzy variables are employed to integrate fuzzy real options and GMCR. Insights into this conflict and general policy suggestions are provided. A potential negotiation support system (NSS) implementing these numerical methods is discussed in the context of negotiating brownfield redevelopment projects. The NSS combines the computational modules, decision support system (DSS) prototypes, and geographic information systems (GIS), and message systems. A public-private partnership (PPP) will be enhanced through information sharing, scenario generation, and conflict analysis provided by the NSS, encouraging more efficient brownfield redevelopment and leading to greater regional sustainability. The integrated usage of fuzzy real options, OWA, and GMCR takes advantage of fuzziness and randomness, making better evaluation technique available in a multiple DMs negotiation setting. Decision techniques expand their range from decision analysis, multiple criteria analysis, to a game-theoretic approach, contributing to a big picture on decision making under uncertainty. When these methods are used to study brownfield redevelopment, we found that creating better business opportunities, such as allowing land use change to raise net income, are more important in determining equilibria than remediation cost refunding. Better redevelopment policies can be proposed to aid negotiations among stakeholders

Hierarchical risk assessment of water supply systems

Water supply systems are usually designed, constructed, operated, and managed in an open environment, thus they are inevitably exposed to varied uncertain threats and conditions. In order to evaluate the reliability of water supply systems under threatened conditions, risk assessment has been recognised as a useful tool to identify threats, analyse vulnerabilities and risks, and select proper mitigation measures. However, due to the complexity and uncertainty of water supply systems and risks, consistent and effective assessments are hard to accomplish by using available risk techniques. With respect to this, the current study develops a new method to assess the risks in complex water supply systems by reconsidering the organisation of risk information and risk mechanism based on the concepts of object-oriented approach. Then hierarchical assessments are conducted to evaluate the risks of components and the water supply system. The current study firstly adopts object-oriented approach, a natural and straightforward mechanism of organising information of the real world systems, to represent the water supply system at both component and system levels. At the component level, components of a water supply system are viewed as different and functional objects. Associated with each object, there are states transition diagrams that explicitly describe the risk relationships between hazards/threats, possible failure states, and negative consequences. At the system level, the water supply system is viewed as a network composed of interconnected objects. Objectoriented structures of the system represent the whole/part relationships and interconnections between components. Then based on the object states transition diagrams and object-oriented structures, this study develops two types of frameworks for risk assessment, i.e., framework of aggregative risk assessment and framework of fault tree analysis. Aggregative risk assessment is to evaluate the risk levels of components, subsystems, and the overall water supply system. While fault trees are to represent the cause-effect relationships for a specific risk in the system. Assessments of these two frameworks can help decision makers to prioritise their maintenance and management strategies in water supply systems. In order to quantitatively evaluate the framework of aggregative risk, this thesis uses a fuzzy evidential reasoning method to determine the risk levels associated with components, subsystems, and the overall water supply system. Fuzzy sets theory is used to evaluate the likelihood, severity, and risk levels associated with each hazard. Dempster-Shafer theory, a typical evidential reasoning method, is adopted to aggregate the risk levels of multiple hazards along the hierarchy of aggregative risk assessment to generate risk levels of components, subsystems, and the overall water supply system. Although fuzzy sets theory and Dempster-Shafer theory have been extensively applied to various problems, their potential of conducting aggregative risk assessments is originally explored in this thesis. Finally, in order to quantitatively evaluate the cause-effect relationships in a water supply system, fuzzy fault tree analysis is adopted in this study. Results of this analysis are likelihood of the occurrence for a specific event and importance measures of the possible contributing events. These results can help risk analysts to plan their mitigation measures to effectively control risks in the water supply system.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Fuzzy expert systems in civil engineering

Imperial Users onl

Brain Tumor Diagnosis Support System: A decision Fusion Framework

An important factor in providing effective and efficient therapy for brain tumors is early and accurate detection, which can increase survival rates. Current image-based tumor detection and diagnosis techniques are heavily dependent on interpretation by neuro-specialists and/or radiologists, making the evaluation process time-consuming and prone to human error and subjectivity. Besides, widespread use of MR spectroscopy requires specialized processing and assessment of the data and obvious and fast show of the results as photos or maps for routine medical interpretative of an exam. Automatic brain tumor detection and classification have the potential to offer greater efficiency and predictions that are more accurate. However, the performance accuracy of automatic detection and classification techniques tends to be dependent on the specific image modality and is well known to vary from technique to technique. For this reason, it would be prudent to examine the variations in the execution of these methods to obtain consistently high levels of achievement accuracy. Designing, implementing, and evaluating categorization software is the goal of the suggested framework for discerning various brain tumor types on magnetic resonance imaging (MRI) using textural features. This thesis introduces a brain tumor detection support system that involves the use of a variety of tumor classifiers. The system is designed as a decision fusion framework that enables these multi-classifier to analyze medical images, such as those obtained from magnetic resonance imaging (MRI). The fusion procedure is ground on the Dempster-Shafer evidence fusion theory. Numerous experimental scenarios have been implemented to validate the efficiency of the proposed framework. Compared with alternative approaches, the outcomes show that the methodology developed in this thesis demonstrates higher accuracy and higher computational efficiency