The main focus of this paper is the novel use of Artificial Intelligence (AI) in natural disaster, more specifically flooding, to improve flood resilience and preparedness. Different types of flood have varying consequences and are followed by a specific pattern. For example, a flash flood can be a result of snow or ice melt and can occur in specific geographic places and certain season. The motivation behind this research has been raised from the Building Resilience into Risk Management (BRIM) project, looking at resilience in water systems. This research uses the application of the state-of-the-art techniques i.e., AI, more specifically Machin Learning (ML) approaches on big data, collected from previous flood events to learn from the past to extract patterns and information and understand flood behaviours in order to improve resilience, prevent damage, and save lives. In this paper, various ML models have been developed and evaluated for classifying floods, i.e., flash flood, lakeshore flood, etc. using current information i.e., weather forecast in different locations. The analytical results show that the Random Forest technique provides the highest accuracy of classification, followed by J48 decision tree and Lazy methods. The classification results can lead to better decision-making on what measures can be taken for prevention and preparedness and thus improve flood resilience.

There is a clear and evident requirement for a conscious effort to be made towards a resilient water system-of-systems (SoS) within the UK, in terms of both supply and flooding. The impact of flooding goes beyond the immediately obvious socio-aspects of disruption, cascading and affecting a wide range of connected systems. The issues caused by flooding need to be treated in a fashion which adopts an SoS approach to evaluate the risks associated with interconnected systems and to assess resilience against flooding from various perspectives. Changes in climate result in deviations in frequency and intensity of precipitation; variations in annual patterns make planning and management for resilience more challenging. This article presents a verified model-based system engineering methodology for decision-makers in the water sector to holistically, and systematically implement resilience within the water context, specifically focusing on effects of flooding on water supply. A novel resilience viewpoint has been created which is solely focused on the resilience aspects of architecture that is presented within this paper. Systems architecture modelling forms the basis of the methodology and includes an innovative resilience viewpoint to help evaluate current SoS resilience, and to design for future resilient states. Architecting for resilience, and subsequently simulating designs, is seen as the solution to successfully ensuring system performance does not suffer, and systems continue to function at the desired levels of operability. The case study presented within this paper demonstrates the application of the SoS resilience methodology on water supply networks in times of flooding, highlighting how such a methodology can be used for approaching resilience in the water sector from an SoS perspective. The methodology highlights where resilience improvements are necessary and also provides a process where architecture solutions can be proposed and tested.

The inherent nature of a Systems of Systems (SoS) makes it very difficult to model and analyze it through conventional means. One of the first challenges faced is how to represent the SoS in a form that lends itself to detailed analysis, especially when full details of the component systems may not be readily available. Therefore, an important consideration is whether use of model abstractions can be sufficient to deal with many of the analysis needs of the SoS. It is clear there is a need for a new paradigm, encompassing methodology, models, tools and flows that enable the future engineering of SoS in order that they can be operated effectively. This paper describes how we are using architecture patterns to architect and analyze SoS in order that we can compare different architecture solutions and provide guidelines for the development of a future architectures based on the analysis of existing architectures. Insights are given to show the benefits for SoS architecture analysis with exemplars taken from a test case dealing with emergency response for a major incident in the UK. Our findings show the significant increase in SoS characterization that patterns can afford the systems architect in all phases of SoS evolution in order to deliver improved SoS capability

A key requirement for an integrated digital tool chain is secure access and control of data assets. Not all stakeholders will have the same access to or control over the flow of information, some will be able to input or change data whilst others will only be able to read the data. Simply providing secure access protocols is not sufficient because copied data can quickly become disassociated and modified from its original instantiation, leading to its reuse elsewhere or later in the lifecycle but in an inappropriate way. Therefore, data management mechanisms are required that capture information about the data along with any decisions or modifications it has undergone during the course of its life, thus providing complete traceability for later validation purposes. This undertaking is essential across the systems engineering lifecycle. This pursuit involves controlling who can access and modify data within the lifecycle. This paper describes a solution to this by the introduction of blockchain technology, a relatively new technology that allows digital information to be distributed but not copied, making it an immutable set of time-stamped data managed by a network of connected systems and services. Though blockchain technology is not commonly referred to when discussing Industry 4.0, the technology’s capabilities should add value when applied in a context of data management and security within the lifecycle of a product or services and in conjunction with digital twins, big data, and IoT. This paper describes how permissioned blockchains can be implemented within a systems engineering lifecycle, providing example architecture patterns showing how data provenance can be maintained throughout.

This paper describes a novel hackathon-style system engineering process and its value as an agile approach to the rapid generation and development of early design concepts of complex engineered products-in this case a future aircraft. Complex product design typically requires a diverse range of stakeholders to arrive at a consensus of key decision criteria and design factors, which requires effective articulation and communication of information across traditional engineering and operational disciplines. The application of the methodology is highlighted by means of a case study inspired by Airbus where stakeholder involvement and internal collaboration among team members were essential to achieve a set of agreed goals. This paper shows that a hackathon grounded on systems engineering approaches and structured around the technical functions within an engineering company has the capability and capacity to communicate a coherent vision and rationale for the conceptual design of a complex engineered product. The hackathon method offers significant benefits to these stakeholders to better manage, prioritize, and decrease excessive complexities in the overall design process. A significant benefit of this agile process is that it can achieve useful results in a very short timeframe (i.e., 80% reduction), where it could take up to a year to accomplish compared with using current/regular internal methods.

Creation and management of large complex systems of systems (SoS) can be a daunting task for even the most experienced engineers. Architecting these systems requires considerable domain expertise and next generation tools to seamlessly link requirements elicitation, through to modeling and simulation, and finally through to implementation. The resulting SoS can be so complex that it is virtually impossible for a single person to understand all its interactions. An important aid to this process is the use of architecture patterns that provide blueprints for proven elements of a system that can be incorporated into the evolution of a system or service. This paper describes a novel approach in validating architecture patterns during the early phases of service or product design and development. We propose an architecture optimization method called Concise Modeling to be used in conjunction with architecture patterns to expedite SoS architecture exploration and analysis.