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No / We propose a new dynamic simulation scheme for large-scale flood hazard modelling and prevention. The approach consists of a number of core parts: Digital terrain modelling with GIS data, Nona-tree space partitions (NTSP), Automatic River object recognition and registration, and a flood spreading model. The digital terrain modelling method allows the creation of a geometric real terrain model for augmented 3D environments with very large GIS data, and it can also use information gathered from aviation and satellite images with a ROAM algorithm. A spatial image segmentation scheme is described for river and flood identification and for a 3D terrain map of flooding region growth and visualisation. The region merging is then implemented by adopting Flood Region Spreading Algorithm (FRSA). Compared with the conventional methods, our approach has the advantages of being capable of realistically visualising the flooding in geometrically-real 3D environments, of handling dynamic flood behaviour in real-time and of dealing with very large-scale data modelling and visualisation.

Energy efficiency is a critical feature of wireless sensor networks (WSNs), because sensor nodes run on batteries that are generally difficult to recharge once deployed. For target tracking---one of the most important WSN application types---energy efficiency needs to be considered in various forms and shapes, such as idle listening, trajectory estimation, and data propagation. In this dissertation, we study three correlated problems on energy efficient target tracking in WSNs: sleep scheduling, particle filtering, and constrained flooding. We develop a Target Prediction and Sleep Scheduling protocol (TPSS) to improve energy efficiency for idle listening. We start with designing a target prediction method based on both kinematics and probability. Based on target prediction and proactive wake-up, TPSS precisely selects the nodes to awaken and reduces their active time, so as to enhance energy efficiency with limited tracking performance loss. In addition, we expand Sleep Scheduling to Multiple Target Tracking (SSMTT), and further reduce the energy consumption by leveraging the redundant alarm messages of interfering targets. Our simulation-based experimental studies show that compared to existing protocols such as Circle scheme and MCTA, TPSS and SSMTT introduce an improvement of 25% ~ 45% on energy efficiency, at the expense of only 5% ~ 15% increase on the detection delay. Particle Filtering is one of the most widely used Bayesian estimation methods, when target tracking is considered as a dynamic state estimation problem for trajectory estimation. However, the significant computational and communication complexity prohibits its application in WSNs. We design two particle filters (PFs)---Vector space based Particle Filter (VPF) and Completely Distributed Particle Filter (CDPF)---to improve energy efficiency of PFs by reducing the number of particles and the communication cost. Our experimental evaluations show that even though VPF incurs 34% more estimation error than RPF, and CDPF incurs a similar estimation error to SDPF, they significantly improve the energy efficiency by as much as 68% and 90% respectively. For data propagation, we present a Constrained Flooding protocol (CFlood) to enhance energy efficiency by increasing the deadline satisfaction ratio per unit energy consumption of time-sensitive packets. CFlood improves real-time performance by flooding, but effectively constrains energy consumption by controlling the scale of flooding---i.e., flooding only when necessary. If unicasting meets the distributed sub-deadline at a hop, CFlood aborts further flooding even after flooding has occurred in the current hop. Our simulation-based experimental studies show that CFlood achieves higher deadline satisfaction ratio per unit energy consumption by as much as 197%, 346%, and 20% than existing multipath forwarding protocols, namely, Mint Routing, MCMP and DFP respectively, especially in sparsely deployed or unreliable sensor network environments. To verify the performance and efficiency of the dissertation's solutions, we developed a prototype implementation based on TelosB motes and TinyOS version 2.1.1. In the field experiments, we compared TPSS, VPF, CDPF, and CFlood algorithms/protocols to their respective competing efforts. Our implementation measurements not only verified the rationality and feasibility of the proposed solutions for target tracking in WSNs, but also strengthened the observations on their efficiency from the simulation. / Ph. D.

No / This evidence review was commissioned as part of the Joint Research Programme project ‘Working Together to Adapt to a Changing Climate: Flood and Coast’ (2018 to 2021). The project is a response to concerns about the impacts of climate change and the likelihood of significantly higher levels of risk to communities due to increased flooding (including inland) or coastal erosion. It aims to produce new learning about, and enhanced guidance for, community engagement practice in situations where this might be particularly challenging, for example, in situations where there is a low likelihood of building or maintaining flood defences in the medium to long term. / Environment Agency / The publisher requests that no file be uploaded. However, the latest version of the full file is available on the Government website on the link above.