Use of artificial intelligence to improve resilience and preparedness against adverse flood events

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

A model-based engineering process to explore resilience attributes in systems-of-systems

Systems-of-systems (SoS) are an ongoing focus of many organisations interested in the development of products and services in environments with great uncertainty. SoS are heavily interconnected entities which comprise of a vast number of constituent parts, both technical and socio,that are inherently complex and must demonstrate levels of resilience. Designing for resilience in SoS has been a great challenge due to the nature of such systems. With no overarching management to steer the SoS in a directed manner, there is a need to investigate novel processes and methods to better understand resilience within a SoS context. This thesis aims to develop new processes to aid systems engineers, and industry  practitioners to understand and design for resilience from a SoS perspective.  Resilience in this instance is regarded at the SoS-level  where the underpinning connected systems demonstrate resilience in the form of a range of supporting properties which lead to improved/ sustained performance. These properties (which are commonly referred to as non-functional properties or “ilities”) will be referred to throughout this thesis as resilience attributes and are  seen as designable features which can be architected at  the early phases of SoS development. Such attributes include reliability, redundancy, flexibility, availability, and safety. The research methodology applied was participatory research to explore the requirements for a model-based engineering process to understand resilience  and to explore SoS resilience attributes.  The methodology was applied to capture requirements for a set of processes that are reflective of real-world   problems within real industrial organisations. An extensive application of case study investigations covered SoS from multiple domains with the inclusion of industry and subject  matter  experts (SMEs) to elicit requirements for a SoS-focused resilience  process and a novel architecture viewpoint. The four case studies were of the classification directed and acknowledged for they have higher levels of  control within them and that can be directly evolved by  leading stakeholders to implement changes such as increased capabilities and increase resilience. The case  studies were conducted in the domains of emergency response, water supply systems and the air transportation system.  [Continued ...]</p

Realizing the role of permissioned blockchains in a systems engineering lifecycle

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

Supplementary Information Files for "Use of Artificial Intelligence to Improve Resilience and Preparedness Against Adverse Flood Events"

Supplementary Information Files for "Use of Artificial Intelligence to Improve Resilience and Preparedness Against Adverse Flood Events"Abstract: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</div

Supplementary Information for "A Model-Based Engineering Methodology and Architecture for Resilience in Systems-of-Systems: A Case of Water Supply Resilience to Flooding."

Supplementary Information for "A Model-Based Engineering Methodology and Architecture for Resilience in Systems-of-Systems: A Case of Water Supply Resilience to Flooding."Abstract: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.</div