Design principles of integrated information platform for emergency responses: The case of 2008 Beijing Olympic Games

This paper investigates the challenges faced in designing an integrated information platform for emergency response management and uses the Beijing Olympic Games as a case study. The research methods are grounded in action research, participatory design, and situation-awareness oriented design. The completion of a more than two-year industrial secondment and six-month field studies ensured that a full understanding of user requirements had been obtained. A service-centered architecture was proposed to satisfy these user requirements. The proposed architecture consists mainly of information gathering, database management, and decision support services. The decision support services include situational overview, instant risk assessment, emergency response preplan, and disaster development prediction. Abstracting from the experience obtained while building this system, we outline a set of design principles in the general domain of information systems (IS) development for emergency management. These design principles form a contribution to the information systems literature because they provide guidance to developers who are aiming to support emergency response and the development of such systems that have not yet been adequately met by any existing types of IS. We are proud that the information platform developed was deployed in the real world and used in the 2008 Beijing Olympic Games. © 2012 INFORMS

Extracting knowledge on slope behaviour from acoustic emission measurements

Early warning systems for slope instability need to alert users of accelerating slope deformation behaviour to enable safety-critical decisions to be made. Field trials of acoustic emission (AE) monitoring of slopes have demonstrated conclusively that generated AE rates are proportional to slope deformation rates, and AE monitoring can be an effective approach to detect accelerating movements and communicate warnings to users. AE is becoming an accepted monitoring technology for geotechnical applications; however, challenges still exist to develop widely applicable interpretation strategies. In this paper, data from a field trial at Hollin Hill, North Yorkshire, UK and a large-scale experiment are used to develop strategies to extract knowledge on slope behaviour from AE measurements. Machine learning approaches for automated interpretation (warning trigger levels and quantifying rates of slope movement) are developed and demonstrated. A conceptual framework for extracting knowledge from AE measurements for use in decision-making is presented.</p

Machine learning prediction of landslide deformation behaviour using acoustic emission and rainfall measurements

Knowledge of landslide displacement trends is important to understand risks and establish early warning trigger thresholds so that action can be taken to protect people and critical infrastructure. However, the availability of direct continuous displacement measurements is often limited due to relatively high costs. This has driven research to establish models that quantify relationships between landslide displacements and other measured parameters such as pore water pressures, rainfall and more recently acoustic emission (AE), so that displacement can be predicted, and hence made available at a lower cost. This paper describes an investigation of established machine learning models to predict displacements using time series measurements of AE and rainfall. Data from a case study site has been used to train models using measured displacements and then test to assess prediction accuracy. The LASSO-ELM model was shown to perform best. It was able to predict displacements to a mean absolute percentage error < 2.5% up to 60 days after the end of the training period, which is better than similar reported studies. Training a LASSO-ELM model using continuous high resolution AE measurements combined with rainfall data has potential to provide predicted displacement trends once direct measurement of displacement is no-longer available

Automatic classification of landslide kinematics using acoustic emission measurements and machine learning

Founded on understanding of a slope’s likely failure mechanism, an early warning system for instability should alert users of accelerating slope deformation behaviour to enable safety-critical decisions to be made. Acoustic emission (AE) monitoring of active waveguides (i.e. a steel tube with granular internal/external backfill installed through a slope) is becoming an accepted monitoring technology for soil slope stability applications; however, challenges still exist to develop widely applicable AE interpretation strategies. The objective of this study was to develop and demonstrate the use of machine learning (ML) approaches to automatically classify landslide kinematics using AE measurements, based on the standard landslide velocity scale. Datasets from large-scale slope failure simulation experiments were used to train and test the ML models. In addition, an example field application using data from a reactivated landslide at Hollin Hill, North Yorkshire, UK, is presented. The results show that ML can automatically classify landslide kinematics using AE measurements with the accuracy of more than 90%. The combination of two AE features, AE rate and AE rate gradient, enable both velocity and acceleration classifications. A conceptual framework is presented for how this automatic approach would be used for landslide early warning in the field, with considerations given to potentially limited site-specific training data

A typhoon shelter selection and evacuee allocation model: a case study of Macao (SAR), China

Typhoon disaster represent one of the most prominent threats to public safety in the Macao Special Administrative Region (SAR) of China and can cause severe economic losses and casualties. Prior to the landing of typhoons, affected people should be evacuated to shelters as soon as possible; this is crucial to prevent injuries and deaths. Various models aim to solve this problem, but the characteristics of disasters and evacuees are often overlooked. This study proposes a model based on the influence of a typhoon and its impact on evacuees. The model’s objective is to minimize the total evacuation distance, taking into account the distance constraint. The model is solved using the spatial analysis tools of Geographic Information Systems (GIS). It is then applied in Macao to solve the evacuation process for Typhoon Mangkhut 2018. The result is an evacuee allocation plan that can help the government organize evacuation efficiently. Furthermore, the number of evacuees allocated to shelters is compared with shelter capacities, which can inform government shelter construction in the future

Disruption of emergency response to vulnerable populations during floods

Emergency responders must reach urgent cases within mandatory timeframes, regardless of weather conditions. However, flooding of transport networks can add critical minutes to travel times between dispatch and arrival. Here, we explicitly model the spatial coverage of all Ambulance Service and Fire and Rescue Service stations in England during flooding of varying severity under compliant response times. We show that even low-magnitude floods can lead to a reduction in national-level compliance with mandatory response times and this reduction can be even more dramatic in some urban agglomerations, making the effectiveness of the emergency response particularly sensitive to the expected impacts of future increases in extreme rainfall and flood risk. Underpinning this sensitivity are policies leading to the centralization of the Ambulance Service and the decentralization of the Fire and Rescue Service. The results provide opportunities to identify hotspots of vulnerability (such as care homes, sheltered accommodation, nurseries and schools) for optimizing the distribution of response stations and developing contingency plans for stranded sites.<br