Objective assessment of the neonatal outcome of labour is important, but it is a difficult and challenging problem. It is an invaluable source of information which can be used to provide feedback to clinicians, to audit a unit's overall performance, and can guide subsequent neonatal care. Current methods are inadequate as they fail to distinguish damage that occurred during labour from damage that occurred before or after labour. Analysis of the chemical acid-base status of blood taken from the umbilical cord of an infant immediately after delivery provides information on any damage suffered by the infant due to lack of oxygen during labour. However, this process is complex and error prone, and requires expertise which is not always available on labour wards. A model of clinical expertise required for the accurate interpretation of umbilical acid-base status was developed, and encapsulated in a rule-based expert system. This expert system checks results to ensure their consistency, identifies whether the results come from arterial or venous vessels, and then produces an interpretation of their meaning. This 'crisp' expert system was validated, verified and commercially released, and has since been installed at twenty two hospitals all around the United Kingdom. The assessment of umbilical acid-base status is characterised by uncertainty in both the basic data and the knowledge required for its interpretation. Fuzzy logic provides a technique for representing both these forms of uncertainty in a single framework. A 'preliminary' fuzzy-logic based expert system to interpret error-free results was developed, based on the knowledge embedded in the crisp expert system. Its performance was compared against clinicians in a validation test, but initially its performance was found to be poor in comparison with the clinicians and inferior to the crisp expert system. An automatic tuning algorithm was developed to modify the behaviour of the fuzzy model utilised in the expert system. Sub-normal membership functions were used to weight terms in the fuzzy expert system in a novel manner. This resulted in an improvement in the performance of the fuzzy expert system to a level comparable to the clinicians, and superior to the crisp expert system. Experimental work was carried out to evaluate the imprecision in umbilical cord acid-base parameters. This information, in conjunction with fresh knowledge elicitation sessions, allowed the creation of a more comprehensive fuzzy expert system, to validate and interpret all acid-base data. This 'integrated' fuzzy expert system was tuned using the comparison data obtained previously, and incorporated vessel identification rules and interpretation rules, with numeric and linguistic outputs for each. The performance of each of the outputs was evaluated in a rigorous validation study. This demonstrated excellent agreement with the experts for the numeric outputs, and agreement on a par with the experts for the linguistic outputs. The numeric interpretation produced by the fuzzy expert system is a novel single dimensional measure that accurately represents the severity of acid-base results. The development of the crisp and fuzzy expert systems represents a major achievement and constitutes a significant contribution to the assessment of neonatal outcome.

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Cognitive Maps (CMs) have shown promise as tools for modelling and simulation of knowledge in computers as representation of real objects, concepts, perceptions or events and their relations. This thesis examines the application of fuzzy theory to the expression of these relations, and investigates the development of a framework to better manage the operations of these relations. The Fuzzy Cognitive Map (FCM) was introduced in 1986 but little progress has been made since. This is because of the difficulty of modifying or extending its reasoning mechanism from causality to relations other than causality, such as associative and deductive reasoning. The ability to express the complex relations between objects and concepts determines the usefulness of the maps. Structuring these concepts and relations in a model so that they can be consistently represented and quickly accessed and anipulated by a computer is the goal of knowledge representation. This forms the main motivation of this research. In this thesis, a novel framework is proposed whereby single-antecedent fuzzy rules can be applied to a directed graph, and reasoning ability is extended to include noncausality. The framework provides a hierarchical structure where a graph in a higher layer represents knowledge at a high level of abstraction, and graphs in a lower layer represent the knowledge in more detail. The framework allows a modular design of knowledge representation and facilitates the creation of a more complex structure for modelling and reasoning. The experiments conducted in this thesis show that the proposed framework is effective and useful for deriving inferences from input data, solving certain classification problems, and for prediction and decision-making.

Vulnerability to weather-related hazards is a considerable humanitarian, economic and environmental concern for cities, especially in developing countries. However, there is a limited understanding of urban vulnerability and its specific implications. This study assesses the spatio-temporal vulnerability caused by climatic and societal change in Chile’s key coastal urban areas. In this urban vulnerability assessment, both regional and local approaches were undertaken, the former to give a broad sense of the possible futures that these cities face and the latter to explore, using all available and reliable data, how climatic and societal change affected one of these metropolitan areas. For the time points 2025, 2055 and 2085, the regional assessment shows that vulnerability is likely to vary across different scenarios and time frames. A significant future increase in exposure to hazards is mainly moderated, to a greater or lesser extent, by an increase in the adaptive capacity of the cities in question. Cities in central and southern Chile are more vulnerable. The local assessment provides a detailed evaluation of recent past vulnerabilities in the Concepción Metropolitan Area (CMA). In the local assessment, an urban indicator framework was first designed and then employed to explore changes in exposure and sensitivity of areas within CMA and the general ability of the urban system to adapt to different hazards. Five weather-related hazards were explored: coastal flooding, fluvial flooding, water scarcity, heat stress and wildfire, using a flexible methodology based on spatial fuzzy modelling with geographic information systems. Hazard-specific vulnerability and overall vulnerability indices were created. The local assessment results indicate a high vulnerability in the CMA that decreased slightly between 1992 and 2002. The combined socio-economic factors of sensitivity and adaptive capacity influenced the index more than the biophysical factors of exposure. Changes in age structure and economic growth had a greater influence on vulnerability that other variables. Overall vulnerability varied across municipalities and hazards, with wildfires and water scarcity influencing overall vulnerability the most. Fuzzy modelling enabled realism and flexibility in the standardization and aggregation of indicators with different attributes. It permitted the exploration of the individual and aggregate influence of the indicators that comprise the indices. ArcGIS software favoured transparency and simplicity in the aggregation of multiple entry criteria, facilitating spatial representation through maps, which can help identify indicators, components and hazards or combinations thereof that influence municipal vulnerability. The results can be used to improve and promote dialogue among policy-makers and stakeholders regarding the prioritization of resources for urban development in ways that can reduce vulnerability to climate change.