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Use of artificial intelligence to improve resilience and preparedness against adverse flood eventsThe 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 methodology and architecture for resilience in systems-of-systems: a case of water supply resilience to floodingThere 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
Surface Stability in Drylands is Influenced by Dispersal Strategy of Soil BacteriaMicrobial adaptations for survival and dispersal may directly influence landscape stability and potential for dust emission in drylands where biological soil crusts (biocrusts) protect mineral soil surfaces from wind erosion. In the Lake Eyre basin of central Australia we operated a wind tunnel on sandy soils and collected the liberated material, which was subjected to DNA sequencing to identify the microbial community composition. Microbial composition of entrained dust was compared with that of the source sand dune soil in addition to nearby claypan and nebkha soils, and water channels which together form a recycling sediment transport system. Wind was found to preferentially liberate 359 identified taxa from sand dunes whereas 137 identified taxa were found to resist wind erosion. Water channel communities included many taxa in common with the soil samples. We hypothesise that the ease with which soil microbes become airborne is often linked to whether the organism is adapted for dispersal by wind or vegetative growth, and that biocrust organisms found in water channels may sometimes use a fluvial dispersal strategy which exploits rare flooding events to rapidly colonise vast pans which are common in drylands. We explain likely geomorphic implications of microbial dispersal strategies which are a consequence of organisms engineering the environment to provide their particular needs. By identifying microbes fitting expectations for these dispersal strategies based on differential abundance analyses, we provide a new perspective for understanding the role of microbiota in landscape stability.