Big data managing in a landslide early warning system: Experience from a ground-based interferometric radar application

A big challenge in terms or landslide risk mitigation is represented by increasing the resiliency of society exposed to the risk. Among the possible strategies with which to reach this goal, there is the implementation of early warning systems. This paper describes a procedure to improve early warning activities in areas affected by high landslide risk, such as those classified as critical infrastructures for their central role in society. This research is part of the project LEWIS (Landslides Early Warning Integrated System): An Integrated System for Landslide Monitoring, Early Warning and Risk Mitigation along Lifelines. LEWIS is composed of a susceptibility assessment methodology providing information for single points and areal monitoring systems, a data transmission network and a data collecting and processing center (DCPC), where readings from all monitoring systems and mathematical models converge and which sets the basis for warning and intervention activities. The aim of this paper is to show how logistic issues linked to advanced monitoring techniques, such as big data transfer and storing, can be dealt with compatibly with an early warning system. Therefore, we focus on the interaction between an areal monitoring tool (a ground-based interferometric radar) and the DCPC. By converting complex data into ASCII strings and through appropriate data cropping and average, and by implementing an algorithm for line-of-sight correction, we managed to reduce the data daily output without compromising the capability for performing

Design, Implementation and Testing of a Network-Based Earthquake Early Warning System in Greece

In this study we implemented and tested the Earthquake Early Warning system PRESTo (PRobabilistic and Evolutionary early warning System, Satriano et al., 2011) on the Greek Ionian islands of Lefkada, Zakynthos and Kefalonia. PRESTo is a free and open source platform for regional Earthquake Early Warning developed at the University of Naples Federico II, which is currently under experimentation in Southern Italy, in the area covered by the Irpinia Seismic Network. The three Ionian islands selected for this study are located on the North-Western part of the Hellenic trench. Here the seismicity rate and the seismic hazard, coupled with the vulnerability of existing critical infrastructures, make this region among the highest seismic risk areas in Europe, where the application of Earthquake Early Warning systems may become a useful strategy to mitigate the potential damage caused by earthquakes. Here we studied the feasibility of implementing an Earthquake Early Warning system on an existing seismic network, which was not specifically made for earthquake early warning purposes, and evaluated the performance of the system, using a data set of real-earthquake recordings. We first describe the technical details of the implementation of PRESTo in the area of interest, including the preliminary parameter configuration and the empirical scaling relationship calibration. Then we evaluated the performance of the system through the off-line analysis of a database of real earthquake records belonging to the most recent M > 4.0 earthquakes occurred in the area. We evaluated the performance in terms of source parameter estimation (location, magnitude), accuracy of ground shaking prediction and lead-time analysis. Finally, we show the preliminary results of the real-time application of PRESTo, performed during the period 01–31 July 2019

Toward Global Drought Early Warning Capability - Expanding International Cooperation for the Development of a Framework for Monitoring and Forecasting

Drought has had a significant impact on civilization throughout history in terms of reductions in agricultural productivity, potable water supply, and economic activity, and in extreme cases this has led to famine. Every continent has semiarid areas, which are especially vulnerable to drought. The Intergovernmental Panel on Climate Change has noted that average annual river runoff and water availability are projected to decrease by 10 percent-13 percent over some dry and semiarid regions in mid and low latitudes, increasing the frequency, intensity, and duration of drought, along with its associated impacts. The sheer magnitude of the problem demands efforts to reduce vulnerability to drought by moving away from the reactive, crisis management approach of the past toward a more proactive, risk management approach that is centered on reducing vulnerability to drought as much as possible while providing early warning of evolving drought conditions and possible impacts. Many countries, unfortunately, do not have adequate resources to provide early warning, but require outside support to provide the necessary early warning information for risk management. Furthermore, in an interconnected world, the need for information on a global scale is crucial for understanding the prospect of declines in agricultural productivity and associated impacts on food prices, food security, and potential for civil conflict. This paper highlights the recent progress made toward a Global Drought Early Warning Monitoring Framework (GDEWF), an underlying partnership and framework, along with its Global Drought Early Warning System (GDEWS), which is its interoperable information system, and the organizations that have begun working together to make it a reality. The GDEWF aims to improve existing regional and national drought monitoring and forecasting capabilities by adding a global component, facilitating continental monitoring and forecasting (where lacking), and improving these tools at various scales, thereby increasing the capacity of national and regional institutions that lack drought early warning systems or complementing existing ones. A further goal is to improve coordination of information delivery for drought-related activities and relief efforts across the world. This is especially relevant for regions and nations with low capacity for drought early warning. To do this requires a global partnership that leverages the resources necessary and develops capabilities at the global level, such as global drought forecasting combined with early warning tools, global real-time monitoring, and harmonized methods to identify critical areas vulnerable to drought. Although the path to a fully functional GDEWS is challenging, multiple partners and organizations within the drought, forecasting, agricultural, and water-cycle communities are committed to working toward its success

A model-based early warning system for runoff-generated debris-flow occurrence: Preliminary results

Early warning systems for debris flows are low cost measures for mitigating this kind of hazard. The early warning systems provide a timely alert for upcoming events in order to take protective measures, such as closing railways-roads, evacuating people from the threatened areas, and put rescue forces into readiness. These systems usually are sensor-based, and the alert time is the interval between the timing of the first detachment of debris flow by a sensor and its arrival into the threatened area. At the purpose of increasing the alert time, we propose an early warning system based on a model-cascade: nowcasting, hydrological- and triggering models. Nowcasting anticipates rainfall pattern that is transformed into runoff by the hydrological model. The triggering model estimates the volume of sediments that the runoff can entrain, and compares it with a critical threshold. If this is exceeded the alert is launched. The proposed early warning system is tested against the available data of the Rovina di Cancia (Northeast Italy) site

Study on the combined threshold for gully-type debris flow early warning

Gully-type debris flow induced by high-intensity and short-duration rainfall frequently causes great loss of properties and causalities in mountainous regions of southwest China. In order to reduce the risk by geohazards, early warning systems have been provided. A triggering index can be detected in an early stage by the monitoring of rainfall and the changes in physical properties of the deposited materials along debris flow channels. Based on the method of critical pore pressure for slope stability analysis, this study presents critical pore pressure threshold in combination with rainfall factors for gully-type debris flow early warning. The Wenjia gully, which contains an enormous amount of loose material, was selected as a case study to reveal the relationship between the rainfall and pore pressure by field monitoring data. A three-level early warning system (zero, attention, and warning) is adopted and the corresponding judgement conditions are defined in real time. Based on this threshold, there are several rainfall events in recent years have been validated in Wenjia gully, which prove that such a combined threshold may be a reliable approach for the early warning of gully-type debris flow to safeguard the population in the mountainous areas.</p

How can a secondary coastal city in Cambodia be better prepared for climate change and natural disaster risks?

This policy brief discusses the readiness of Khemarak Phumin city in Koh Kong province (Cambodia) to cope with impacts of climate change and natural disasters. It provides policy recommendations derived from study findings. Climate vulnerability is due to the absence of an early warning system, the lack of a responsible specialized agency at the city level, no designated evacuation route or safe areas during emergencies, and no reserve emergency fund. These are critical mechanisms in which to invest. Cambodia is prone to both natural and human-made disasters such as flooding, droughts, storms, fire, riverbank collapses, and pest epidemics

Development, implementation and evaluation of an early warning system improvement programme for children in hospital : the PUMA mixed-methods study

Background: The Paediatric early warning system Utilisation and Morbidity Avoidance (PUMA) study was commissioned to develop, implement and evaluate a paediatric track-and-trigger tool for widespread adoption. Following findings from three systematic reviews, revised aims focused on implementation of a whole-systems improvement programme. Objectives: (1) Identify, through systematic review, the following: evidence for core components of effective paediatric track-and-trigger tools and paediatric early warning systems, and contextual factors consequential for paediatric track-and-trigger tool and early warning system effectiveness. (2) Develop and implement an evidence-based paediatric early warning system improvement programme (i.e. the PUMA programme). (3) Evaluate the effectiveness of the PUMA programme by examining clinical practice and core outcomes trends. (4) Identify ingredients of successful implementation of the PUMA programme. Review methods: The quantitative reviews addressed the following two questions: how well validated are existing paediatric track-and-trigger tools and their component parts for predicting inpatient deterioration? How effective are paediatric early warning systems (with or without a tool) at reducing mortality and critical events? The qualitative review addressed the following question: what sociomaterial and contextual factors are associated with successful or unsuccessful paediatric early warning systems (with or without tools)? Design: Interrupted time series and ethnographic case studies were used to evaluate the PUMA programme. Qualitative methods were deployed in a process evaluation. Setting: The study was set in two district general and two tertiary children’s hospitals. Intervention: The PUMA programme is a paediatric early warning system improvement programme designed to harness local expertise to implement contextually appropriate interventions. Main outcome measures: The primary outcome was a composite metric, representing children who experienced one of the following in 1 month: mortality, cardiac arrest, respiratory arrest, unplanned admission to a paediatric intensive care unit or unplanned admission to a high-dependency unit. Paediatric early warning system changes were assessed through ethnographic ward case studies. Results: The reviews showed limited effectiveness of paediatric track-and-trigger tools in isolation, and multiple failure points in paediatric early warning systems. All sites made paediatric early warning system changes; some of the clearer quantitative findings appeared to relate to qualitative observations. Systems changed in response to wider contextual factors. Limitations: Low event rates made quantitative outcome measures challenging. Implementation was not a one-shot event, creating challenges for the interrupted time series in conceptualising ‘implementation’ and ‘post-intervention’ periods. Conclusions: Detecting and acting on deterioration in the acute hospital setting requires a whole-systems approach. The PUMA programme offers a framework to support ongoing system-improvement work; the approach could be used more widely. Organisational-level system change can affect clinical outcomes positively. Alternative outcome measures are required for research and quality improvement. Future work: The following further research is recommended: a consensus study to identify upstream indicators of paediatric early warning system performance; an evaluation of OUTCOME approach in other clinical areas; an evaluation of supernumerary nurse co-ordinator role; and an evaluation of mandated system improvement. Study registration: This study is registered as PROSPERO CRD42015015326

Development of an earthquake early warning system using real-time strong motion signals

As urbanization progresses worldwide, earthquakes pose serious threat to lives and properties for urban areas near major active faults on land or subduction zones off shore. Earthquake Early Warning (EEW) can be a useful tool for reducing earthquake hazards, if the spatial relation between cities and earthquake sources is favorable for such warning and their citizens are properly trained to respond to earthquake warning messages. An EEW system forewarns an urban area of forthcoming strong shaking, normally with a few sec to a few tens of sec of warning time, i.e., before the arrival of the destructive S-wave part of the strong ground motion. Even a few second of advanced warning time will be useful for pre-programmed emergency measures for various critical facilities, such as rapid-transit vehicles and high-speed trains to avoid potential derailment; it will be also useful for orderly shutoff of gas pipelines to minimize fire hazards, controlled shutdown of high-technological manufacturing operations to reduce potential losses, and safe-guarding of computer facilities to avoid loss of vital databases. We explored a practical approach to EEW with the use of a ground-motion period parameter τ_c and a high-pass filtered vertical displacement amplitude parameter Pd from the initial 3 sec of the P waveforms. At a given site, an earthquake magnitude could be determined from τ_c and the peak ground-motion velocity (PGV) could be estimated from Pd. In this method, incoming strong motion acceleration signals are recursively converted to ground velocity and displacement. A P-wave trigger is constantly monitored. When a trigger occurs, τ_c and Pd are computed. The earthquake magnitude and the on-site ground-motion intensity could be estimated and the warning could be issued. In an ideal situation, such warnings would be available within 10 sec of the origin time of a large earthquake whose subsequent ground motion may last for tens of seconds

Construction and Application of “Active Prediction-Passive Warning” Joint Impact Ground Pressure Resilience Prevention System: Take the Kuan Gou Coal Mine as an Example

AbstractWith the increasing depth and intensity of coal mining, the impact on ground pressure has become one of the main disasters facing mining, seriously threatening mine safety. Introducing the concept of toughness urban design, building a joint toughness prevention and control system based on active prediction and analysis of the impact pressure risk at the back mining face according to the geological deposit conditions and mining technology conditions and passive warning using monitoring data to explore the impact precursor characteristics is an important basis for impact pressure management and has important engineering significance to ensure the safe back mining. In this paper, firstly, the whole working face is divided into small unit areas, and the BP neural network prediction model is constructed to predict and analyze each small unit separately, and the distribution of impact ground pressure hazard level in different areas of the working face is derived. Next, a FLAC numerical model was established to analyze the stress distribution and migration characteristics at different retrieval distances of the working face and to explore the main distribution areas of impact hazard. Finally, the trend method, critical value method, and dynamic rate of change method were applied to determine the early warning indicators of impact ground pressure in the Kuan Gou coal mine, establish a comprehensive early warning method of impact ground pressure applicable to the Kuan Gou coal mine, and carry out field application with good effect. The findings of this paper have good scientific significance and reference value for promoting impact hazard analysis and early warning in mines with similar geological conditions and mining technology conditions in China