Application of Artificial Intelligence Approaches in the Flood Management Process for Assessing Blockage at Cross-Drainage Hydraulic Structures

Floods are the most recurrent, widespread and damaging natural disasters, and are ex-pected to become further devastating because of global warming. Blockage of cross-drainage hydraulic structures (e.g., culverts, bridges) by flood-borne debris is an influen-tial factor which usually results in reducing hydraulic capacity, diverting the flows, dam-aging structures and downstream scouring. Australia is among the countries adversely impacted by blockage issues (e.g., 1998 floods in Wollongong, 2007 floods in Newcas-tle). In this context, Wollongong City Council (WCC), under the Australian Rainfall and Runoff (ARR), investigated the impact of blockage on floods and proposed guidelines to consider blockage in the design process for the first time. However, existing WCC guide-lines are based on various assumptions (i.e., visual inspections as representative of hy-draulic behaviour, post-flood blockage as representative of peak floods, blockage remains constant during the whole flooding event), that are not supported by scientific research while also being criticised by hydraulic design engineers. This suggests the need to per-form detailed investigations of blockage from both visual and hydraulic perspectives, in order to develop quantifiable relationships and incorporate blockage into design guide-lines of hydraulic structures. However, because of the complex nature of blockage as a process and the lack of blockage-related data from actual floods, conventional numerical modelling-based approaches have not achieved much success. The research in this thesis applies artificial intelligence (AI) approaches to assess the blockage at cross-drainage hydraulic structures, motivated by recent success achieved by AI in addressing complex real-world problems (e.g., scour depth estimation and flood inundation monitoring). The research has been carried out in three phases: (a) litera-ture review, (b) hydraulic blockage assessment, and (c) visual blockage assessment. The first phase investigates the use of computer vision in the flood management domain and provides context for blockage. The second phase investigates hydraulic blockage using lab scale experiments and the implementation of multiple machine learning approaches on datasets collected from lab experiments (i.e., Hydraulics-Lab Dataset (HD), Visual Hydraulics-Lab Dataset (VHD)). The artificial neural network (ANN) and end-to-end deep learning approaches reported top performers among the implemented approaches and demonstrated the potential of learning-based approaches in addressing blockage is-sues. The third phase assesses visual blockage at culverts using deep learning classifi-cation, detection and segmentation approaches for two types of visual assessments (i.e., blockage status classification, percentage visual blockage estimation). Firstly, a range of existing convolutional neural network (CNN) image classification models are imple-mented and compared using visual datasets (i.e., Images of Culvert Openings and Block-age (ICOB), VHD, Synthetic Images of Culverts (SIC)), with the aim to automate the process of manual visual blockage classification of culverts. The Neural Architecture Search Network (NASNet) model achieved best classification results among those im-plemented. Furthermore, the study identified background noise and simplified labelling criteria as two contributing factors in degraded performance of existing CNN models for blockage classification. To address the background clutter issue, a detection-classification pipeline is proposed and achieved improved visual blockage classification performance. The proposed pipeline has been deployed using edge computing hardware for blockage monitoring of actual culverts. The role of synthetic data (i.e., SIC) on the performance of culvert opening detection is also investigated. Secondly, an automated segmentation-classification deep learning pipeline is proposed to estimate the percentage of visual blockage at circular culverts to better prioritise culvert maintenance. The AI solutions proposed in this thesis are integrated into a blockage assessment framework, designed to be deployed through edge computing to monitor, record and assess blockage at cross-drainage hydraulic structures

Human Resource Management in Emergency Situations

The dissertation examines the issues related to the human resource management in emergency situations and introduces the measures helping to solve these issues. The prime aim is to analyse complexly a human resource management, built environment resilience management life cycle and its stages for the purpose of creating an effective Human Resource Management in Emergency Situations Model and Intelligent System. This would help in accelerating resilience in every stage, managing personal stress and reducing disaster-related losses. The dissertation consists of an Introduction, three Chapters, the Conclusions, References, List of Author’s Publications and nine Appendices. The introduction discusses the research problem and the research relevance, outlines the research object, states the research aim and objectives, overviews the research methodology and the original contribution of the research, presents the practical value of the research results, and lists the defended propositions. The introduction concludes with an overview of the author’s publications and conference presentations on the topic of this dissertation. Chapter 1 introduces best practice in the field of disaster and resilience management in the built environment. It also analyses disaster and resilience management life cycle ant its stages, reviews different intelligent decision support systems, and investigates researches on application of physiological parameters and their dependence on stress. The chapter ends with conclusions and the explicit objectives of the dissertation. Chapter 2 of the dissertation introduces the conceptual model of human resource management in emergency situations. To implement multiple criteria analysis of the research object the methods of multiple criteria analysis and mahematics are proposed. They should be integrated with intelligent technologies. In Chapter 3 the model developed by the author and the methods of multiple criteria analysis are adopted by developing the Intelligent Decision Support System for a Human Resource Management in Emergency Situations consisting of four subsystems: Physiological Advisory Subsystem to Analyse a User’s Post-Disaster Stress Management; Text Analytics Subsystem; Recommender Thermometer for Measuring the Preparedness for Resilience and Subsystem of Integrated Virtual and Intelligent Technologies. The main statements of the thesis were published in eleven scientific articles: two in journals listed in the Thomson Reuters ISI Web of Science, one in a peer-reviewed scientific journal, four in peer-reviewed conference proceedings referenced in the Thomson Reuters ISI database, and three in peer-reviewed conference proceedings in Lithuania. Five presentations were given on the topic of the dissertation at conferences in Lithuania and other countries

Progress in Landslide Research and Technology, Volume 1 Issue 1, 2022

This open access book provides an overview of the progress in landslide research and technology and is part of a book series of the International Consortium on Landslides (ICL). The book provides a common platform for the publication of recent progress in landslide research and technology for practical applications and the benefit for the society contributing to the Kyoto Landslide Commitment 2020, which is expected to continue up to 2030 and even beyond to globally promote the understanding and reduction of landslide disaster risk, as well as to address the 2030 Agenda Sustainable Development Goals

Progress in Landslide Research and Technology, Volume 1 Issue 2, 2022

This open access book provides an overview of the progress in landslide research and technology and is part of a book series of the International Consortium on Landslides (ICL). It gives an overview of recent progress in landslide research and technology for practical applications and the benefit for the society contributing to understanding and reducing landslide disaster risk

Citizen Science: Reducing Risk and Building Resilience to Natural Hazards

Natural hazards are becoming increasingly frequent within the context of climate change—making reducing risk and building resilience against these hazards more crucial than ever. An emerging shift has been noted from broad-scale, top-down risk and resilience assessments toward more participatory, community-based, bottom-up approaches. Arguably, non-scientist local stakeholders have always played an important role in risk knowledge management and resilience building. Rapidly developing information and communication technologies such as the Internet, smartphones, and social media have already demonstrated their sizeable potential to make knowledge creation more multidirectional, decentralized, diverse, and inclusive (Paul et al., 2018). Combined with technologies for robust and low-cost sensor networks, various citizen science approaches have emerged recently (e.g., Haklay, 2012; Paul et al., 2018) as a promising direction in the provision of extensive, real-time information for risk management (as well as improving data provision in data-scarce regions). It can serve as a means of educating and empowering communities and stakeholders that are bypassed by more traditional knowledge generation processes. This Research Topic compiles 13 contributions that interrogate the manifold ways in which citizen science has been interpreted to reduce risk against hazards that are (i) water-related (i.e., floods, hurricanes, drought, landslides); (ii) deep-earth-related (i.e., earthquakes and volcanoes); and (iii) responding to global environmental change such as sea-level rise. We have sought to analyse the particular failures and successes of natural hazards-related citizen science projects: the objective is to obtain a clearer understanding of “best practice” in a citizen science context

Application of Geographical Information Systems to Lahar Hazard Assessment on an Active Volcanic System

Lahars (highly dynamic mixtures of volcanic debris and water) have been responsible for some of the most serious volcanic disasters and have killed tens of thousands of people in recent decades. Despite considerable lahar model development in the sciences, many research tools have proved wholly unsuitable for practical application on an active volcanic system where it is difficult to obtain field measurements. In addition, geographic information systems are tools that offer a great potential to explore, model and map hazards, but are currently under-utilised for lahar hazard assessment. This research pioneered a three-tiered approach to lahar hazard assessment on Montserrat, West Indies. Initially, requirements of potential users of lahar information (scientists and decision-makers) were established through interview and evaluated against attainable modelling outputs (given flow type and data availability). Subsequently, a digital elevation model, fit for modelling lahars, was used by a path of steepest descent algorithm and a semi-empirical debris-flow model in the prediction of lahar routes and inundation areas. Limitations of these established geographical information system (GIS) based models, for predicting the behaviour of (relatively under-studied) dilute lahars, were used to inform key parameters for a novel model, also tightly coupled to a GIS, that simulated flow routes based on change in velocity. Importantly, uncertainty in model predictions was assessed through a stochastic simulation of elevation error. Finally, the practical utility of modelling outputs (visualisations) was assessed through mutual feedback with local scientists. The new model adequately replicated past flow routes and gave preliminary predictions for velocities and travel times, thus providing a short-term lahar hazard assessment. Inundation areas were also mapped using the debris-flow model to assist long-term planning. Ultimately, a GIS can support ‘on the ground’ planning decisions, but efficacy is limited by an active volcanic system which can restrict feedback to and from end-users. *[The appendices for this thesis were submitted as separate files which could not be uploaded to the repository. Please contact the author for more information.]

Present and future resilience research driven by science and technology

Community resilience against major disasters is a multidisciplinary research field that garners an ever-increasing interest worldwide. This paper provides summaries of the discussions held on the subject matter and the research outcomes presented during the Second Resilience Workshop in Nanjing and Shanghai. It, thus, offers a community view of present work and future research directions identified by the workshop participants who hail from Asia – including China, Japan and Korea; Europe and the Americas

PREdictive model for DISaster response configuration (PREDIS decision platform)

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe extraordinary conditions of a disaster, require the mobilisation of all available resources, inducing the rush of humanitarian partners into the affected area. This phenomenon called the proliferation of actors, causes serious problems during the disaster response phase including the oversupply, duplicated efforts, lack of planning. The aim of this research is to provide a solution to reduce the partner proliferation problem. To that end the main research question is put forward as “How to reduce the proliferation of partners in a disaster response”? Panel analysis of the historic record of 4,252 natural onset disasters between 1980 to 2013 via regression analysis, MA and AHP gives rise to the formation of a predictive decision-making platform called PREDIS. It is capable of predicting the human impact of the disaster (fatality, injured, homeless) of up to 3% of errors and enables the decision makers to estimate the required needs for each disaster and prioritises them based on the disaster type and socio-economics of the affected country. It further renders it possible to rank and optimise the desired partners based on the decision maker’s preferences. Verification of the PREDIS through a simulation game design using a sample group of decision makers, show that this technique enables the user to decide within one hour after the disaster strike using the widely available data at the time of the disaster. It also enables non-experts to decide almost identically to experts in terms of the similarity of the choices and the speed of the decision.The lack of an extensive database for the potential humanitarian partners from which to choose, is the limitation of this research in addition to the lack of standardised set of minimum requirements for the suitable partners.The model is also as strong as its data feed which is inconsistent in various humanitarian sources

Real-time early warning system design for pluvial flash floods - A review

[EN] Pluvial flash floods in urban areas are becoming increasingly frequent due to climate change and human actions, negatively impacting the life, work, production and infrastructure of a population. Pluvial flooding occurs when intense rainfall overflows the limits of urban drainage and water accumulation causes hazardous flash floods. Although flash floods are hard to predict given their rapid formation, Early Warning Systems (EWS) are used to minimize casualties. We performed a systematic review to define the basic structure of an EWS for rain flash floods. The structure of the review is as follows: first, Section 2 describes the most important factors that affect the intensity of pluvial flash floods during rainfall events. Section 3 defines the key elements and actors involved in an effective EWS. Section 4 reviews different EWS architectures for pluvial flash floods implemented worldwide. 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