Post-event Analysis Research Articles

A Lightweight Future Skeleton Generation Network(FSGN) Based on Spatio-Temporal Encoding and Decoding

Since early warning in industrial applications is far more valuable than post-event analysis, human activity prediction based on partially observed skeleton sequences has become a popular research area. Recent studies focus on building complex deep learning networks to generate accurate future skeleton data, but overlook the requirement for timeliness. Different from such frame-by-frame generation methods, we propose a Future Skeleton Generation Network (FSGN) based on spatio-temporal encoding and decoding framework. Firstly, we design a dynamically regulated input module to ensure equal-length input of partially observed data, and set modules like discrete cosine transform(DCT) and low-pass filtering(LPF) to filter important information. Then, we employ an improved multi-layer perceptron(MLP) structure as the basic computational unit for the encoding and decoding framework to extract spatio-temporal information, and propose using multi-dimensional motion error of human skeleton to form the loss function. Finally, we use an output module symmetrical to the input module to achieve the generation of future activity data. Results show that the proposed FSGN achieves fewer parameters(0.12M) and higher generation accuracy, which can effectively provide future information for human activity prediction tasks.

A spectral assessment for instant preliminary evaluation of structures after seismic events

This article introduces a simple, efficient spectral-based approach for rapid preliminary seismic assessment of earthquake-affected structures. Performance is validated using data from three highly earthquake-affected structures in New Zealand, where visual inspection with subjective outcomes missed damage, resulting in inaccurate and delayed decisions with significant social and economic losses. The three structures considered include (1) the instrumented Bank of New Zealand (BNZ) building; (2) the un-instrumented Queensgate Mall (QM) Complex; and (3) the un-instrumented Canterbury Television (CTV) building. This study uses these cases to highlight the importance of structural health monitoring (SHM) instrumentation and reliable quantified post-earthquake assessment methods in earthquake-prone areas, where each damaging earthquake and subsequent further-damaging aftershocks demand continuous monitoring to continuously assess damage and life safety risk. The simple, low-cost spectral analyses in this study clearly show the existence of damage and deterioration not fully discovered with standard visual inspection methods. This outcome highlights the importance of sensor networks and SHM instrumentation so quantitative, post-event analysis can rapidly augment and target further, more subjective visual inspection results.

Media-Based Post-Event Impact Analysis of the 2021 Heat Dome in Canada.

The unprecedented 2021 Heat Dome caused wide-ranging and long-lasting impacts in western Canada, including 619 confirmed heat-related deaths in British Columbia, a doubling of emergency medical calls, increased hospitalisations, infrastructure failures and stress on plants and animals. However, such varied socio-economic consequences of extreme heat can be challenging to capture using a single post-event analysis method. Therefore, there is a need to explore alternative approaches and data sources. Using the 2021 Heat Dome as a case study, a post-event analysis using online news media articles (n = 2909) from 5 subscription news databases and a grey literature search was conducted to identify the socio-economic impacts of the extreme heat event in Canada. The articles reported a wide range of effects to the natural environment (n = 1366), social infrastructure and services (n = 1121), human health (n = 1074), critical infrastructure (n = 988) and the private sector (n = 165). The media-based post-event analysis captured various impacts, some of which have not been identified through other data sources and approaches. Overall, we show that media analysis can complement traditional post-event analysis methods and provide additional perspectives to governments and public health and safety officials.

Avoided wildfire impact modeling with counterfactual probabilistic analysis.

Assessing the effectiveness and measuring the performance of fuel treatments and other wildfire risk mitigation efforts are challenging endeavors. Perhaps the most complicated is quantifying avoided impacts. In this study, we show how probabilistic counterfactual analysis can help with performance evaluation. We borrow insights from the disaster risk mitigation and climate event attribution literature to illustrate a counterfactual framework and provide examples using ensemble wildfire simulations. Specifically, we reanalyze previously published fire simulation data from fire-prone landscapes in New Mexico, USA, and show applications for post-event analysis as well as pre-event evaluation of fuel treatment scenarios. This approach found that treated landscapes likely would have reduced fire risk compared to the untreated scenarios. To conclude, we offer ideas for future expansions in theory and methods.

Digital twin-driven identification of fault situation in distribution networks connected to distributed wind power

The gradual integration of distributed wind power into distribution networks presents significant challenges for identifying faults, as it requires accurate and timely fault identification based on large-scale data. This paper proposes a novel fault situation identification framework driven by digital twins to overcome the existing bottleneck of asynchronous online fault identification and offline post-event analysis. The framework employs a high-precision digital twin avatar with parallel strategies of fully electromagnetic transient calculation to obtain the real-time operation status of distribution networks connected with distributed wind power. Furthermore, the framework employs skip-connected dilated causal convolution to mine meaningful multi-time-scale features from massive data, while using an automatic hyper-parameter tuning strategy. A case study based on IEEE 33-node standard distribution network and a real-world distribution network demonstrates the effectiveness of this framework, achieving ultra-real-time and high-precision simulation and effective fault identification even under noise or missing data, accelerating traditional post-fault analysis to microsecond-level situation identification.

Analysis of phases and medalists to women’s singles matches in badminton at the Tokyo 2020 Olympic Games

Purpose: The purpose of this study is to determine the technical and tactical styles of the world's best badminton players at the Tokyo 2020 Olympic Games. Materials and methods: The research material consisted of the post-event analysis of 58 matches from the Tokyo Olympics. Through AHP analysis (analytic hierarchy process) to 6 temporal variables with a high influence on match/game effectiveness were selected. Match/game statistical analysis, with the one-way measure of the variance (ANOVA), and independent-sample T-test, were conducted by using IBM SPSS for Windows version 22.0 (IBM. Corp. Armonk, NY) in order to compare the statistical differences. Results: This conducted study showed that frequency of strokes in badminton was 1-1.12 strokes/sec, which is one of the longest rally strokes. The mean and standard deviation of the women's singles players, particularly in the knockout stage, is greater than the mean and standard deviation of the group stage. Results show the differences between Group Phase (35.05±9.77, 47.07±13.12, s, P=0.020) and Eliminatory Phase (11.93±1.58, 13.93±2.30, s, P=0.030) respectively. Conclusions: The women's singles competition currently mainly revolves around the technical and tactical styles of "fast-oriented", "offensive-oriented" and “self-oriented”. This is in line with the current trend of development of the world's women's singles athletes' more "masculine" style of play.

Remote Characterization of the 12 January 2020 Eruption of Taal Volcano, Philippines, Using Seismo-Acoustic, Volcanic Lightning, and Satellite Observations

ABSTRACT On 12 January 2020, an eruption began on the shores of the Main Crater Lake (MCL) of Taal Volcano—a caldera system on the southern end of Luzon Island in the Philippines. Taal, one of the most active volcanoes in the Philippines, is located 30 km south of Manila—a major metropolitan area with a population of 13.5 million people. Eruptive activity intensified throughout the day on 12 January, producing prolific volcanic lightning, ashfall, and a sustained plume that reached 16–17 km altitude. The chronology of events was well documented by the Philippine Institute of Volcanology and Seismology and the Tokyo Volcanic Ash Advisory Center. The wealth of data collected during the eruption provides a unique opportunity to investigate how the combination of different remote sensing methods may complement local observations and monitoring. Remote systems tend to provide lower resolution data but are also less likely to be compromised by the eruptive activity, thus providing continuous records of eruptive processes. Here, we present a postevent analysis of the 12 January activity, including data from long-range lightning, infrasound, and seismic arrays located at distances up to several thousands of kilometers from the volcano. By combining these datasets, we distinguish five phases of activity and infer a major shift in eruption behavior around 12:00 on 12 January (UTC). The remote observations suggest that the most of the water within the MCL (∼42 million m3) was vaporized and incorporated into the volcanic plume within the first 12 hr of the eruption.

Filling observational gaps with crowdsourced citizen science rainfall data from the Met Office Weather Observation Website

Abstract This paper demonstrates the potential for crowdsourced rainfall data to infill gaps in the official rain gauge network and to provide new datasets for use in research. We use data from the Met Office Weather Observation Website (WOW) over 10 years (2011–2020) to generate two open-source datasets for Britain; multi-parameter raw data in an easy-to-use format; and an hourly rainfall dataset. We have compiled and prepared the data and detail here station selection, rain depth calculation, and data resampling to hourly intervals to create a consistent dataset for further processing (including statistical quality control) and application. Mapping the new rainfall dataset establishes that WOW observations fill spatial gaps in the official ground-based rain gauge network over Britain, particularly in urban areas. This could be particularly useful for post-event analysis of rainfall that results in pluvial flash flooding. Here, we focus on Britain but due to agreements with meteorological services in Belgium, the Netherlands, Australia, New Zealand, Sweden, and the Republic of Ireland, plus many citizen scientists globally opting to share data via WOW, there is potential for the development of similar datasets using these methods around the world.

28th September 2018 Mw 7.5 Sulawesi Supershear Earthquake, Indonesia: Ground effects and macroseismic intensity estimation using ESI-2007 scale

The 28th September 2018 Sulawesi Supershear earthquake (MW 7.5) was one of the deadliest earthquakes in the recent history of Indonesia causing ∼4000 causalities. The earthquake caused a ∼ 177 km long surface rupture along the Palu-Karo fault. Apart from surface rupture, the earthquake caused extensive earthquake environmental effects (EEEs) around the Palu-Donggala area of Central Sulawesi, Indonesia, which includes tsunami, coastal landslide, liquefaction, ground cracks and more than 7300 landslides in hilly areas. Initial post-event analysis and reports assigned a Modified Mercalli Intensity (MMI) of VII to VIII in Palu City and the surrounding area. Building damage and ground effects caused by the earthquake suggested that seismic intensity was understated. Here we applied the EEEs information from field survey data, published reports, and remote sensing tools to determine macroseismic intensity using the Environmental Seismic Intensity (ESI-07) Scale. The ESI-07 intensity derived from the ground effects suggests the maximum intensity of X-XI, which is 3–4° higher than the traditional intensity estimated by the United States Geological Survey (USGS) and the Indonesian Agency for Meteorology, Climatology, and Geophysics (BMKG). ShakeMaps were generated considering the ESI-07 values. The ShakeMap was compared with the instrumentally derived ShakeMap for the Palu earthquake, which proves that the ShakeMap prepared from the instrumental data or structural damage data is underrated. We argue that proper documentation of the EEEs is necessary for such damaging earthquakes for future earthquake hazard mapping and planning in the study area and other earthquakes in Indonesia. In addition, this will help in defining the on-fault and off-fault damage zone towards reducing the seismic risk of the Palu Donggala area.

Parallel incremental association rule mining framework for public opinion analysis

Internet public opinion association rule mining (POARM) has garnered significant attention from a larger group of netizens. However, most POARM methods have been applied to post-event analysis, which has poor timeliness and a low efficiency. Therefore, the real-time monitoring of public opinion association rules is lacking. To address this problem, we propose the parallel Incremental POARM Framework (IPOARM), which improves the timeliness of association rule mining in two ways: 1) using an incremental merge method to consider both inserted and deleted public opinion transaction sets and reuse previous frequent itemsets to reduce redundant computation and 2) employing a parallel implementation of big data process platforms. Moreover, the flexible association rule mining (ARM) algorithm selection structure of IPOARM enables users to freely select suitable ARM algorithms. We represent four classic transaction sets as public opinion transaction sets and compare the IPOARM framework with two novel incremental association rules mining algorithms. Our evaluations indicate that the IPOARM framework can discover Internet public opinion association rules quickly, implying that it can be easily integrated into existing big data processing platforms and that it significantly improves the mining accuracy and efficiency by 12.756% and 29.371%, respectively.

A Machine Learning Approach to Fault Prediction of Power Distribution Grids under Heatwaves

Climate change is increasing the occurrence of the so-called heatwaves with a trend that is expected to worsen in the next years due to global warming. The growing intensity and duration of these extreme weather events are leading to a significant number of power system failures, especially in urban areas. This is drastically affecting the reliability and normal operation of power distribution grids around the world, with high financial costs and huge negative impacts on people's life. Typically, the response to these failure events is approached by post-event analysis, aimed at identifying the grid areas that require resources to increase the resilience of the system and prevent future outages. Nevertheless, understanding the nature of heatwaves and forecasting their impact on power distribution systems can be useful to anticipate them and accelerate a reaction, possibly avoiding negative impacts on power systems and customers. In this study, a structured method to predict distribution grid disruptions caused by heatwaves is defined. The proposed method relies on machine learning to analyze previous failure data and forecast power grid outages using operational and meteorological information. The method is evaluated using real failure data from a large power distribution network located in southern Italy.

A systematic approach to the peat machines and equipment classification development

The «National Security Strategy of the Russian Federation until 2030» prioritises the use of resource-saving and waste-free technologies for natural resource extraction and processing, import substitution of mining equipment in the Russian mining sector, and the introduction of digital technologies at all stages of resource extraction and processing in the mining industry to improve their safety. The aim of the article is to study the gradual development of peat machinery classification and the relevance of its improvement through integrated mechanization devices to create waste-free extraction and processing of peat deposits using fullcycle mobile complexes with the development of environmentally friendly and resource-saving technologies for peat production. The methodological basis of the research includes post-event analysis, peat machine design theory, and systems analysis. As a research result, new system factors influencing the development of the classification of currently available machinery and equipment for peat production, as well as classification variants combining the processes of extraction and processing of peat deposit resources are provided, which allow modeling the structure of full-cycle mobile complexes for extraction and processing of peat deposit resources without waste. In terms of practical application, the classification of peat machinery enables the development of a rational decision-making data system for optimizing the structure of the technological machinery and equipment fleet of peat extraction enterprises, taking into account the deteriorating conditions of peat resources and development technologies, the economic conditions of the industry and the current trends of digitalization in the extractive industry.

FIRE PLANNING OF URBAN-RURAL INTERFACE IN OPEN SOURCE GIS ENVIRONMENT: CASE STUDY OF THE APULIA REGION (SOUTHERN ITALY)

Abstract. Fires represent one of main challenges of the last decades as global changes are causing an increase in economic and environmental damages. Indeed, just in 2017, more than 10,000 km2 of land were burned in Europe, causing significant damage to both the natural heritage (25% of burned areas were part of Natura 2000 protected areas) and the economy with estimated losses around 10 billion euros. In addition, every year there are losses of human life that make even more necessary new strategies of action and monitoring. Therefore, an efficient management of forecasting, prevention, active fight and post fire phases, is essential to make the territories less vulnerable and reduce the impacts on human lives. But these steps require an integrated approach of different tools in order to make faster and more efficient the different operations. In this context, the study illustrates the expeditious and standardized methodologies in open source GIS environment proposed in a research project with the Civil Protection of Apulia Region in order to implement a vulnerability index to improve operations in forecasting, emergency management in real-near time and post-event analysis in urban-rural interface. All the techniques and methodologies proposed were based on the use of QGIS software as it is a highly user friendly software that can be easily used even by non-specialized technicians. Moreover, the methodologies have been validated through a direct comparison with the tools currently in use in Civil Protection Department of Apulia Region.

The Updated Version of the A.Ne.Mo.S. GLE Alert System: The Case of the Ground-Level Enhancement GLE73 on 28 October 2021

A ground-level enhancement (GLE) event is a sudden increase in cosmic ray intensity originated by solar sources and recorded by ground-based detectors. GLEs are invariably associated with large solar flares that can release and accelerate solar particles at high energies. The minimum kinetic energy of particles reaching the Earth’s surface is >433 MeV at sea level and about 300 MeV/n at high-mountain altitude of about 3000 m a.s.l. Even though these abrupt events linked to solar activity are quite rare, they can have a great impact on technological systems and human health when recorded. Therefore, the accurate and effective prognosis of such events is of great importance. In this paper, an overview of the most recently recorded GLE event and the first of solar cycle 25, i.e., GLE73, as well as a post-event analysis is presented. GLE73 was detected on 28 October 2021 and was associated with the active region AR12887 on the central part of the solar disk, which produced an X1.0 solar flare. The event was registered by several stations of the worldwide ground-based neutron monitor network. An accurate alert was issued successfully by the ESA R-ESC federated product GLE Alert Plus, as well as the updated GLE Alert++ System of the Athens Neutron Monitor Station (A.Ne.Mo.S.). It should be emphasized that the GLE Alert++ signal by NKUA/A.Ne.Mo.S. was issued 45 min earlier than the one issued by GOES. A short description and the advantages of this last system are provided.

Improving Power Grid Resilience Under Extreme Weather Conditions With Proper Regulation and Management of DERs—Experiences Learned From the 2021 Texas Power Crisis

Due to climate change in the past few decades, extreme weather conditions have become more frequent and caused power outages with enormous damage to the well-being and the economy. Affected by extremely cold weather, the 2021 Texas power crisis deployed the most significant firm load shedding in US history, costing the economy $10 billion to $20 billion via direct and indirect loss. The North American Electric Reliability Organization (NERC), Federal Energy Commission (FERC), and other literature studies conducted post-event analysis from the perspective of conventional power systems’ planning and operation, and little discussion was made on the distributed energy resources (DERs). Based on the actual data on the 2021 Texas power crisis, this study analyzed the role of DERs in this event and showed the importance of effective regulation and management in improving power grid resilience under such extreme weather conditions.

The Experimental Validation of a Digital Filter Mathematical Modelling for Nuclear Fuel Vibration Monitoring System

Aiming at vehicles will produce shocks and vibrations for various reasons in the process of transportation and handling, any shock and vibration that exceeds the regulations would cause damage to the nuclear fuel being transported. In order to monitor and analyze the shock and vibration data scientifically, a monitoring system based on MEMS acceleration sensor technology is developed to measure shocks and vibrations. The measured result involves some noise caused by the sensitivity of the sensor and the electrical noise, and a digital filter mathematical modelling is introduced to reduce noise and improve the measured result accuracy. In order to verify the effectiveness of the device designed in this paper, the device and the high-precision seismic acceleration sensor AC73 are, respectively, used for field tests; the results show that the test precision of the designed device can meet the requirements, and also, the designed shock and vibration monitoring system can record and store the three-axis acceleration, temperature, humidity, pressure, time, and other parameters that exceed the set threshold during nuclear fuel transportation in real time and complete the fuel assembly transportation status assessment and promptly remind the transportation personnel to take safety precautions and treatment measures. Therefore, it can be widely used in the monitoring, recording, and postevent data analysis of the transportation process of important products such as nuclear fuel.

Retrospective Result Analysis of Land Reforms in the Russian Federation

The existing system of land relations requires better organizational and economic mechanisms, search for new ways to increase its efficiency and competitiveness. Land reforms in Russia focused on land privatization transformed the entire system of land relations. Undeveloped state regulation and the difficult financial situation discouraged most agricultural producers from reproducing land resources. The present paper aims to conduct a post-event analysis of land tenure strategies, regulatory and legal acts and scientific and methodological support of land reforms in Russia. The existing approaches to studying theoretical and methodological issues of land relations regulation, shortcomings in methodological and legal support, the practical need for new methods and tools for effective land management in the agricultural sector have predetermined the relevance and significance of the research topic. Research target is the territory of the Russian Federation, the creation of a system of land ownership and land use adapted to the market economy. The study involved analyzing statistical data on agricultural production in combination with quantitative and qualitative indicators of land resources. The work provides a wealth of experience in land transformations, planning and forecasting the Russian territories' socio-economic development, and working out land management methods, which can be applied in other countries.

Earthquake-induced landslide monitoring and survey by means of InSAR

Abstract. This study uses interferometric synthetic aperture radar (SAR) techniques to identify and track earthquake-induced landslides as well as lands prone to landslides, by detecting deformations in areas struck by earthquakes. The pilot study area investigates the Mila region in Algeria, which suffered significant landslides and structural damage (earthquake: Mw 5, 7 August 2020). DInSAR analysis shows normal interferograms with small fringes. The coherence change detection (CCD) and DInSAR analysis were able to identify many landslides and ground deformations also confirmed by Sentinel-2 optical images and field inspection. The most important displacement (2.5 m), located in the Kherba neighborhood, caused severe damage to dwellings. It is worth notice that CCD and DInSAR are very useful since they were also able to identify ground cracks surrounding a large zone (3.94 km2 area) in Grarem City, whereas the Sentinel-2 optical images could not detect them. Although displacement time-series analysis of 224 interferograms (April 2015 to September 2020) performed using LiCSBAS did not detect any pre-event geotechnical precursors, the post-event analysis shows a 110 mm yr−1 subsidence velocity in the back hillside of Kherba.

A Block-Coordinate-Descent Robust Approach to Incentive-Based Integrated Demand Response in Managing Multienergy Hubs With Must-Run Processes

This article presents a new robust incentive-based integrated demand response (IDR) model for energy hub systems (EHSs). The considered incentive-based demand response (IBDR) schemes are interruptible/curtailable service and capacity market program. The proposed IDR model integrates the arbitrage ability of EHS storages as well as energy conversion into the IDR model. The objective of the IDR optimization problem is to maximize/minimize the allocated incentives/penalties in targeted time periods by IBDR schemes while supplying must-run processes with no interruption. Uncertainties of load and energy prices are considered through user-defined polyhedral uncertainty sets. A trilevel robust optimization (RO) is developed, which includes a trilevel min–max–min problem. To solve the trilevel adaptive robust model, the column-and-constraint generation technique is employed by means of a decomposition methodology recasting the trilevel model into a single-level min problem and a bilevel max–min problem. Unlike previous RO models that solve the inner max–min problem by duality theory, a block-coordinate-descent (BCD) methodology is used to solve the max–min problem by means of the first-order Taylor series in this study. The use of the BCD technique instead of duality theory enables a recourse-based characterization of integer variables, such as EHS storage status, which was not applicable in previous models (due to the use of duality theory). Moreover, Lagrange multipliers are eliminated as no duality is conducted. A postevent analysis is conducted to justify the long-term performance of the robust solutions and determine the optimal settings of the BCD robust approach. Results indicate that the IDR model significantly reduces the EHS input electricity in targeted time periods (four hours per day) by IBDR schemes and covers the required electricity with must-run processes by combined heat and power unit, using natural gas. This implies a 2.13% reduction in the operation cost as incentives are obtained through IBDR schemes.

Comprehensive Radar Data for the Contiguous United States: Multi-Year Reanalysis of Remotely Sensed Storms

Abstract The Multi-Year Reanalysis of Remotely Sensed Storms (MYRORSS) dataset blends radar data from the WSR-88D network and Near-Storm Environmental (NSE) model analyses using the Multi-Radar Multi-Sensor (MRMS) framework. The MYRORSS dataset uses the WSR-88D archive starting in 1998–2011, processing all valid single-radar volumes to produce a seamless three-dimensional reflectivity volume over the entire contiguous United States with an approximate 5-min update frequency. The three-dimensional grid has an approximate 1 km × 1 km horizontal dimension and is on a stretched vertical grid that extends to 20 km MSL with a maximal vertical spacing of 1 km. Several reflectivity-derived, severe-storm-related products are also produced, which leverage the ability to merge the MRMS and NSE data. Two Doppler velocity-derived azimuthal shear layer maximum products are produced at a higher horizontal resolution of approximately 0.5 km × 0.5 km. The initial period of record for the dataset is 1998–2011. The dataset underwent intensive manual quality control to ensure that all available and valid data were included while excluding highly problematic radar volumes that were a negligible percentage of the overall dataset, but which caused large data errors in some cases. This dataset has applications toward radar-based climatologies, postevent analysis, machine learning applications, model verification, and warning improvements. Details of the manual quality control process are included and examples of some of these applications are presented.