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Flood is one of the most devastating natural disasters. It is estimated that flooding from sea level rise will cause one trillion USD to major coastal cities of the world by the year 2050. Flood not only destroys the economy, but it also creates physical and psychological sufferings for the human and destroys infrastructures. Disseminating flood warnings and evacuating people from the flood-affected areas help to save human life. Therefore, predicting flood will help government authorities to take necessary actions to evacuate humans and arrange relief for the people. This licentiate thesis focuses on four different aspects of flood prediction using wireless sensor networks (WSNs). Firstly, different WSNs, protocols related to WSN, and backhaul connectivity in the context of predicting flood were investigated. A heterogeneous WSN network for flood prediction was proposed. Secondly, data coming from sensors contain anomaly due to different types of uncertainty, which hampers the accuracy of flood prediction. Therefore, anomalous data needs to be filtered out. A novel algorithm based on belief rule base for detecting the anomaly from sensor data has been proposed in this thesis. Thirdly, predicting flood is a challenging task as it involves multi-level factors, which cannot be measured with 100% certainty. Belief rule based expert systems (BRBESs) can be considered to handle the complex problem of this nature as they address different types of uncertainty. A web based BRBES was developed for predicting flood. This system provides better usability, more computational power to handle larger numbers of rule bases and scalability by porting it into a web-based solution. To improve the accuracy of flood prediction, a learning mechanism for multi-level BRBES was proposed. Furthermore, a comparison between the proposed multi-level belief rule based learning algorithm and other machine learning techniques including Artificial Neural Networks (ANN), Support Vector Machine (SVM) based regression, and Linear Regression has been performed. In the light of the research findings of this thesis, it can be argued that flood prediction can be accomplished more accurately by integrating WSN and BRBES.

MCom (Business Information Systems) / Deparment of Business Information Systems / The extent of success of a given project can be increased by using an appropriate Project Management Methodology (PMM) that takes into account the specific characteristics of the project (such as complexity, size, budget, nature of risk, etc.). PMMs have evolved over the years to become more diverse, complex, with evolving and dynamic ICT platforms. Such PMMs have traditionally been used as frameworks to guide the project management process for decision makers (such as Project Managers, Project Owners and Project Teams). The choice of selecting an appropriate project methodology is daunting; apart from other considerations related to project characteristics such as budget, scope, schedule, performance and resource constraints. One of the vital stages of a successful software development project is selecting a good software development methodology that best suits that project. The aim of this research is to investigate the critical factors to be considered by project managers in the selection of the software development methodology for the project. These critical factors are then used as a foundation for an architecture for an “unbounded rule-based expert system. A survey was conducted amongst project managers to determine the critical factors necessary for the selection of a software development methodology. From the findings of the study, it was established the critical factors revolved around three constructs of Project Excellence Enablers, Excellent Project Management Practices, and Business Value Proposition factors. The findings from this study therefore provided a rationale and a basis for the evolution of an “Unbounded Rule-Based Expert Systems Architecture” as a basis for the selection of the right software development methodology / NRF