Types Of Disasters Research Articles

Global hotspots of climate-related disasters

Climate change mitigation is crucial to prevent excessive temperature rise, a primary contributor to climate-related impacts. However, even if net zero emissions were achieved immediately, the carbon locked in the atmosphere will continue to impact ecosystems, people, settlements and infrastructure, as observed in the past several decades. Despite the urgent need to minimize climate change impacts, climate adaptation has not kept pace with escalating risks. Data on disaster occurrences and impacts can guide action to where it is most needed. We used data on climate-related disasters recorded between 2000 and 2020 in the Emergency Events Database (EM-DAT) to 1) discern disparities in climate-related disaster impacts across countries and continents, and 2) pinpoint administrative areas where people have been highly impacted from those types of disasters. During this period, over 4,600 occurrences of climate-related disasters were documented, directly impacting over 3.3 billion people. Highly developed countries experienced fewer impacts despite not having a lower number of climate-related events. African countries showed an increase in the number of people impacted through time, despite a decrease in the number of climate-related events. Areas in Central America and the Caribbean, Eastern North America, Eastern Africa and Madagascar, and Southern and Eastern China, India and Southeast Asia had the highest numbers of people impacted per km2. Identifying locations with disproportionally high numbers of impacted people can lead to action and policy shifts, from local to international levels. Some of the approaches to adapt to climate change impacts that can be cost-effective and readily available are those based on nature and the benefits that nature provides to people. Therefore, nature conservation, restoration and management could be important interventions to help people adapt to the impacts of climate change, especially in areas of low human development and where people have experienced high and very high climate impacts. In the policy sphere, synthesizing information on historical occurrences of climate-related disasters could guide efforts to address losses and damages and to promote climate justice.

Meteorological disaster disturbances on the main crops in the north‒south transitional zone of China

Global climate change, with warming as its main feature, has altered the spatial-temporal evolution of factors such as precipitation and temperature that can cause meteorological disasters. The complex and changeable climate has led to frequent natural disasters, while the frequency and intensity of extreme climate events have also significantly increased, posing an enormous threat to societal production and human life. As the most important geoecological transitional zone of mainland China, the stability of agricultural production in China’s north–south transitional zone is crucial for ensuring food security under climate change. With the use of daily precipitation and potential evapotranspiration data from 1961 to 2018, this study focused on analysing disturbances such as extreme precipitation and drought disasters at different time scales during the winter wheat and summer maize growing seasons in the north–south transitional zone of China from an agricultural production perspective and attempted to answer the following questions: first, from an agricultural production perspective, what are the temporal and spatial distribution patterns of extreme precipitation and arid climate events in the north–south transitional zone? Second, which areas are at high risk of being disturbed by different types of meteorological disasters and require increased attention? The results indicated that (1) in terms of the overall temporal variation, the degree of extreme precipitation and drought stress faced by agricultural production in the region is decreasing. However, the temporal variation at each station in the north–south transitional zone was not completely consistent with the overall trend, and both increasing and decreasing trends were observed. The sites exhibiting an increase overlapped with typical regions of the north–south transitional zone to varying degrees, indicating that the typical regions represented not only theoretical potential risk areas under climate change but also suffered from meteorological disaster disturbances. (2) The precipitation distribution during the winter wheat growth period in the south–north transitional zone was uneven and varied significantly. High values of extreme precipitation indices during the winter wheat growth period were mainly concentrated in the southern part of the eastern section of the north‒south transitional zone. The precipitation distribution during the summer maize growth period significantly differed, with the highest amount of heavy rain and largest number of rainstorm days concentrated in the southeastern part of the north‒south transitional zone. The spatial distribution of the drought frequency in the north–south transitional zone, as indicated by the monthly standardized precipitation evapotranspiration index (SPEI1), showed that the areas with high total drought frequencies were mainly concentrated in northeast Jiangsu, southeast Henan, and north Anhui, which primarily experienced light drought. The central part of Jiangsu Province exhibited a high frequency of moderate drought, while southern Jiangsu Province and southwestern Shaanxi Province were prone to severe drought. Additionally, southeastern Hebei and eastern Henan were identified as areas with a high frequency of extreme drought. Finally, the central region of Sichuan Province was characterized by both severe and extreme drought conditions. Based on the SPEI12-derived spatial distribution of the drought frequency in the north–south transitional zone, the areas with a high total drought frequency were mainly concentrated in central and eastern Henan, southeast Shaanxi, southeast Shandong, and central Sichuan, which primarily experienced light to moderate drought. The northwestern part of Jiangsu, the southern part of Hebei, and the western part of Shandong are regions with a high frequency of severe drought, while the eastern part of Henan is an area with high frequencies of both severe and extreme drought. (3) High-value areas of extreme precipitation and drought disturbance in the north–south transitional zone overlapped with the edge of the transitional zone to varying degrees. Approximately 63.58% of the north‒south transitional zone of China was characterized by moderate or high stress levels, primarily concentrated along the southern boundary and central core area, and nearly 39.5% of all counties experienced two or more types of disaster stresses.

Analysis of Damage Assessment Tweets During Disaster using Sentiment Analysis

This seems to be an abstract or summary of a paper on monitoring Twitter for damage assessments after a disaster. Using simple linear regression and Support Vector Regression methods for weighting and the random forest methodology for classification, the research provides a novel approach that makes use of low-level lexical characteristics, top-most frequency word features, and syntactic elements relevant to damage assessment. The accuracy of the suggested method for identifying damage assessment tweets is 94.62%, as measured across 14 typical disaster datasets for binary and multi-class categorization. Significant advancements were observed when comparing the proposed method to the state-of-the-art for both in-domain and cross-domain scenarios. The suggested method does not require labelled tweets or tweets of a specific disaster kind in order to be trained and implemented; instead, it can be trained on historical disaster datasets.

Assessment and Comparison of Community Resilience to Floods and Tsunamis in Padang, Indonesia

Community disaster resilience is very important for the City of Padang, where floods occur almost every year. The city is also now at the peak of the Sunda Megathrust cycle, which could cause a tsunami in the near future. This study aims to assess and compare community resilience to different types of natural disasters that cause either shocks or stresses. Data were collected through the distribution of questionnaires to 150 respondents and field observations. The analysis employed a quantitative approach to assess community disaster resilience based on indices of social, community capacity, economic, and infrastructure dimensions. The highest values for the social dimension were found in both floods and tsunamis, scoring equally at 0.73, indicating high resilience. In contrast, the lowest values for the economic dimension were observed in floods (0.35) and tsunamis (0.32), indicating low resilience. A significant difference was noted in the community capacity dimension, with a resilience value of 0.50 for floods and 0.34 for tsunamis, indicating moderate and low resilience, respectively. The results of this analysis suggest that the community is more prepared to face floods, as evidenced by higher values in the community capacity and economic dimensions compared to tsunamis. Therefore, interventions to increase community resilience are needed. This study recommends the need for an integrative and comprehensive approach, beyond existing efforts, in increasing community resilience related to floods and tsunamis.

Government disaster relief services and disaster conflict

This study aimed to determine whether disaster victims' satisfaction with the government's disaster relief services affects disaster conflicts. Subsequently, according to the type of disaster and scale of damage, differences in the impact of disaster relief services on disaster conflict were compared and analyzed. The analysis data are collected from 2311 disaster victims through the “establishment of safe service for disaster victims” by the National Disaster Management Research Institute affiliated with the Ministry of the Interior and Safety in South Korea. A structural equation model and multi-group analysis were used to analyze the data, and the main analysis results are as follows. First, as quantitative and qualitative satisfaction with disaster relief services increased, trust in the government improved. Second, the higher the trust in the government as a disaster management entity, the lower the likelihood of experiencing conflicts with the government. The difference in the relationship between satisfaction with disaster relief services and disaster conflict was revealed according to the type of disaster experienced by the disaster victims and the scale of damage. This study fills the existing academic gap and expands academic externalities related to disaster management by verifying the effects of the aforementioned disaster relief services and conflicts between residents and the government.

Probabilistic forecast of next earthquake event in Makran subduction zone using Weibull distribution

Earthquake is the most lethal type of natural disaster. Researchers have been working to develop precise earthquake prediction methods to save lives. A statistical investigation is an effective earthquake prediction method because they offer more details about the seismic risk or hazard issue. This study utilizes seismic data from the Makran subduction zone from 1934 to 2017. Probability distributions may be employed to assess the risk of seismic events and earthquake occurrence probability. This work estimates the probability of the next major event in the Makran subduction zone through Weibull distribution by considering strong earthquakes with a magnitude (Mw ≥ 6) in the intervals (in years) between two consecutive earthquakes. The probabilities of the forthcoming seismic event have been estimated based on the previous earthquake record, pictorially. The calculated parameters of the Weibull distribution for the Makran subduction zone may help to forecast the probabilities of a strong earthquake and describe the pattern of earthquake average return time. The calculated probability for the Weibull distribution reaches 0.92 after ten years since the last strong earthquake in 2021, indicating that the Weibull distribution within and around the present research area in 2031 will be 92%.

A Deep Parallel Hybrid Fusion Model for disaster tweet classification on Twitter data

Disaster tweet classification has gained significant attention in natural language processing (NLP) due to its potential to aid disaster response and emergency management. The goal of disaster tweet classification is to automate the identification of informative tweets containing information related to various types of disasters, such as floods, earthquakes, wildfires, and more. This classification task plays a crucial role in real-time monitoring, situational awareness, and timely response coordination during emergency situations. In this context, we propose a Deep Parallel Hybrid Fusion Model (DPHFM) that combines features from Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory (Bi-LSTM) as base learners. The features extracted from these base learners are combined using a fusion mechanism and then reconstructed for input to a meta-learner for making predictions. The DPHFM is trained on disaster datasets, such as crisisMMD, which consists of seven natural disaster events. The model underwent thorough evaluation using various metrics, demonstrating an average performance improvement of 90% to 96%. Furthermore, the proposed model’s performance surpassed that of other state-of-the-art models, showcasing its potential for disaster tweet classification using deep learning techniques.

ORGANIZING INFORMATION COLLECTION IN THE CONTEXT OF DISASTER: APPROACHES BEFORE, DURING AND AFTER EMERGENCIES

This article examines the challenges and strategies associated with collecting critical information during different types of disasters. The use of technology and specialized tools to collect data on various natural disasters, such as natural events, industrial accidents, and health crises, is examined. It also emphasizes the importance of cleaning this data to remove unnecessary information. It is shown that by mastering these data collection techniques, more informed decisions can be made and disaster management can be improved.

Social media and crowdsourcing in disaster risk management: Trends, gaps, and insights from the current state of research

AbstractSocial media and crowdsourcing (SMCS) are increasingly used as tools to govern disasters. Nevertheless, we have a limited understanding of how these technologies support disaster risk management (DRM). Based on a comprehensive literature review of 237 papers, we present a state‐of‐the‐art of the research field linking SMCS with DRM. The paper provides insights into major trends in research published from 2008 to 2023. It maps the use of SMCS across disaster phases, disaster types, research design, and geographies before and after the coronavirus disease 2019 (COVID‐19) pandemic. Our results show that existing research predominantly focuses on preparedness and response activities. Moreover, research on SMCS tends to favor (single) case studies and secondary data, and despite a minor shift following the COVID‐19 pandemic, research is dominated by North America, South Asia, Australia, and Europe. There is very little research coming from severely disaster‐prone regions in the Global South on SMCS in disasters with a few exceptions. Research should focus on the power shifts that these technologies produce, the contexts in which they are supposed to be applied, and the sociocultural conditions that co‐produce, potentially vulnerable, outcomes of SMCS in disaster risk management.

Mapping Urban Floods via Spectral Indices and Machine Learning Algorithms

Throughout history, natural disasters have caused severe damage to people and properties worldwide. Flooding is one of the most disastrous types of natural disasters. A key feature of flood assessment has been making use of the information derived from remote-sensing imagery from optical sensors on satellites using spectral indices. Here, a study was conducted about a recent spectral index, the Normalised Difference Inundation Index, and a new ensemble spectral index, the Concatenated Normalised Difference Water Index, and two mature spectral indices: Normalised Difference Water Index and the differential Normalised Difference Water Index with four different machine learning algorithms: Decision Tree, Random Forest, Naive Bayes, and K-Nearest Neighbours applied to the PlanetScope satellite imagery about the Brisbane February 2022 flood which is in urban environment. Statistical analysis was applied to evaluate the results. Overall, the four algorithms provided no significant difference in terms of accuracy and F1 score. However, there were significant differences when some variations in the indices and the algorithms were combined. This research provides a validation of existing measures to identify floods in an urban environment that can help to improve sustainable development.

Construction of yield loss indicators for cold vortex, light-temperature-water combined stress during the flowering period of rice in Northeast China.

The construction of a yield loss evaluation index for the cold vortex type light-temperature-water composite adversity during rice flowering period in Northeast China is important for elucidating the impacts of cold vortex type composite disasters on rice yield loss in middle and high latitude areas. Moreover, it can provide meteorological support to ensure safe production of high-quality japonica rice in China and contribute to regional disaster reduction and efficiency improvement. By combining growth period data, meteorological data, and yield data, we delineated and constructed the composite stress occurrence index of cold vortex type light-temperature-water at the flowering stage of japonica. We analyzed the relationship between factors causing disasters and yield structure, as well as the relationship between different yield structures and yield by employing BP neural network method. We further dissected the processes involved in the causation of combined disasters. Based on the K-means clustering method and historical typical disaster years, we quantified the critical thresholds and disaster grades, and established an evaluation index and model for assessing yield loss caused by combined stress from cold vortex type light-temperature-water. Finally, we examined the spatial and temporal variations of low temperature, abundant rainfall, and reduced sunlight during the flowering period in the three provinces of Northeast China. Results showed that the critical thresholds for light, temperature, and water stress index during the flowering stage of mild, moderate, and severe cold vortex types were [0, 0.21), [0.21, 0.32), and [0.32, 0.64], respectively. The rates of yield loss were [0, 0.03), [0.03, 0.08), and [0.08, 0.096], respectively. Based on the verification results of a total of 751 samples in 11 random years from 1961 to 2020, the percentage of stations for which the production reduction grade, as calculated by the composite index developed in this study, aligning with the actual production reduction grade was 63.7%, consistently exceeding 58.0% annually. Moreover, the proportion of sites with a similarity or difference level of 1 stood at 88.3%, surpassing 85.0% in each year. The index could effectively assess the extent of rice yield loss caused by cold vortex disasters in Northeast China.

Impact of Extratropical Circulation in East Asia on Western North Pacific Tropical Cyclone Frequency and Intensity

Abstract This study examines the East Asia and western North Pacific (WNP) monsoon circulation patterns for strong and weak WNP tropical cyclone (TC) numbers in summer. It suggested that years with more intense TCs are coupled with the tropical monsoon circulations, including the northward cross-equatorial airflow and the extending tropical monsoon trough toward the central-eastern WNP. However, a higher frequency of weak TCs can be largely attributed to the mutual interactions among the tropical monsoon trough west of 140°E, the westward South Asia high, and the high pressure anomaly in Northeast Asia (NEA). Then the potential influence of the NEA extratropical system is focused on. The resultant local negative potential vorticity (PV) anomaly is carried southeastward by the prevailing flow. It stimulates a descending flow around 30°N, which favors the westward retreat of the South Asian high and the decreased zonal vertical wind shear around 20°N. The associated lower-level outflow converges in the tropical WNP and reinforces the ascending motion around 10°–20°N. Meanwhile, the warm air column in NEA also contributes to anomalous easterlies in a band around 30°N, intensifying the lower-level cyclonic vorticity in the northwestern WNP. Consequently, the ascending motion, cyclonic vorticity, and the weakened zonal vertical wind shear in northwestern WNP promote the WTC formation. A set of physically based empirical models is developed using various physically based predictors to reconstruct the number of intense and weak TCs. Cross-validated hindcasts suggest that the NEA extratropical circulation can serve as an additional source of predictability for the weak TC variability. Significance Statement Tropical cyclones (TCs) are a highly destructive type of natural disaster that have garnered widespread attention. By comparison with intense TCs (ITCs), weak TCs (WTCs) are much more numerous and often form closer to the coastal regions of East Asia, whose mechanism has not been fully understood. In this study, we suggest that more ITCs are controlled by tropical monsoon circulations, while the WTC variability is closely coupled with both tropical and extratropical monsoon systems. In addition to the tropical monsoon trough west of 140°E and the westward South Asian high, the Northeast Asian circulation can regulate the WTC number by changing the lower-level vorticity, vertical motion, and vertical wind shear in the WTC genesis-prone region, which can be applied to improve the seasonal prediction skill of WTCs.

Community-Based Strategies for Disaster Preparedness in Mauritius

Mauritius is Small Island Developing State (SIDS) of volcanic origin which lies in the South-West Indian Ocean Basin and is vulnerable to the consequences of global environmental challenges. The fourth priority action of the Sendai Framework for Disaster Risk Reduction 2015-2030 is to consolidate disaster preparedness for an effective response and to have a Build Back Better agenda that will improve recovery, rehabilitation, and reconstruction. This research aimed at examining the disaster management system in Mauritius through community-based approaches. The National Disaster Risk Reduction and Management Centre’s (NDRRMC) involvement with the community living in high-risk zones was investigated. This study used quantitative data gathered from secondary sources and included the number of field training simulation exercises, training of volunteers under the Community Disaster Response Programme (CDRP), awareness campaigns and formulation of contingency plans based on documentation across Mauritius. The NDRRMC identified 109 high-risk zones across Mauritius out of which 13.7 per cent benefitted from all four forms of mitigation activities. Results indicated that contingency plans in Mauritius were evidence-based and had been developed after identification of disaster risk in selected regions, and therefore, they were limited to some types of disasters only. Findings also revealed that flooding was the most common type of simulated disaster exercise undertaken. The greatest number of field simulation exercises was carried out in Poste de Flacq. Results also demonstrated that only 38.5% of the community were exposed to disaster-related awareness campaigns, making it the least common form of disaster risk reduction (DRR) activity in Mauritius.

A Review of Electric UAV Visual Detection and Navigation Technologies for Emergency Rescue Missions

Sudden disasters often result in significant losses of human lives and property, and emergency rescue is a necessary response to disasters. In recent years, with the development of electric unmanned aerial vehicles (UAVs) and artificial intelligence technology, the combination of these technologies has been gradually applied to emergency rescue missions. However, in the face of the complex working conditions of emergency rescue missions, the application of electric UAV visual detection still faces great challenges, particularly in relation to a lack of GPS positioning signal in closed emergency rescue environments, as well as unforeseen obstacle avoidance and autonomous planning and searching flights. Although the combination of visual detection and visual navigation technology shows great potential and added value for use in the context of emergency rescue, at present it remains in the research and experimental stages. Consequently, this paper summarizes and discusses the current status and development of visual detection and navigation technologies for electric UAVs, as well as issues related to emergency rescue applications, with a view to accelerating the research and application of visual detection and navigation technologies for electric UAVs in emergency rescue missions. In this study, we first summarize the classification of typical disasters, analyze the application of sample UAV and configurations in typical disasters with a high frequency of occurrence, refine key electric UAV technologies in emergency rescue missions, and propose the value of exploring electric UAV visual detection and navigation technologies. Subsequently, current research on electric UAV visual detection and navigation technology is analyzed and its application in emergency rescue missions is discussed. Finally, this paper presents the problems faced in the application of electric UAV visual detection and navigation technology in urban emergency rescue environments and offers insights into future research directions.

Natural Disaster Mitigation in Jeneponto Regency Indonesia: A Sociology Perspective

This research analyzes the importance of natural disaster mitigation efforts in the context of Jeneponto District, a region in Indonesia that is vulnerable to various types of natural disasters. The purpose of this research is to describe the roles of stakeholders and disaster management strategies in the Jeneponto District. This study used a qualitative approach, where data were collected through interviews and observations. The results showed that natural disaster management in Jeneponto Regency involved the active role of many stakeholders, including government and NGOs, traditional leaders, and community leaders. This confirms the importance of the role of stakeholders in handling natural disasters in Jeneponto District, both in terms of coordination, resource allocation, and development of mitigation strategies. Efforts are needed to strengthen community preparedness, involve various related parties, and respect local culture in disaster management efforts. The results also show that the social construction of disaster management involves a deep understanding of how disaster management is understood, constructed, and implemented in the community. Although many mitigation strategies have been implemented, there is still a need to strengthen community preparedness and improve coordination between the stakeholders involved. Some effective methods in disaster management include preparedness education, community involvement in mitigation planning, and utilization of technology for early warning systems. This article contributes to policy recommendations for improving adaptation and mitigation capacity at the local level. Practical implications include developing training programs, improving security infrastructure, and increasing community awareness of disaster risks.

MEMPERKUAT KEBERLANJUTAN MASYARAKAT DESA SIRNA JAYA, BOGOR: PELATIHAN KESIAGAAN BENCANA UNTUK KELOMPOK POKDARWIS RAWA GEDE

Disaster preparedness training in Sirna Jaya Village is important to protect the community from the negative impacts of disasters. In this training, participants learn first aid and risk reduction through different types of disasters. This disaster first aid training involves expert lecture sessions, experience-sharing discussions, focus group discussions, and disaster simulations. After the training, weekly monthly monitoring and evaluation were conducted through interviews, group discussions, and direct observation. Evaluation data is used to evaluate programs and provide recommendations for the development of similar programs in the future. The evaluation results showed positive changes in individual and group preparedness, proving the effectiveness of this training. This training increases knowledge and builds the foundation of resilient and disaster-ready communities.

Socialization and Making of Landslide Disaster Warning Equipment in Gentan Village, Sukoharjo

Abstract: This research aims to enhance the awareness and consciousness of the community regarding the potential disasters that may occur in Gentan Village. Various types of natural disasters frequently occur in Gentan Village, one of which is landslides. In this study, a device was created and installed in landslide-prone areas of Gentan Village. The purpose of this device is to provide notifications in the form of a siren when a landslide disaster occurs, with the hope that it will be beneficial to the surrounding community. In the implementation of this activity, Badan Penanggulangan Bencana Daerah (BPBD) participated in providing information about natural disasters and how to mitigate them. BPBD and the local community also supported and actively participated in the installation of landslide warning devices in landslide-prone areas. The impact of this activity has been able to increase the knowledge and awareness of the Gentan Village community regarding landslide disasters. The landslide warning devices are expected to be replicated and further developed to become more effective. Keywords: disaster, warning signal, technology

The Implementation of Fire Disaster Mitigation at Harris Vertu Hotel Jakarta

Purpose: This research aims to find out and explain how the Harris Vertu Jakarta hotel is implemented in dealing with disasters that are likely to occur, especially fire disasters, disasters are something that cannot be predicted, and knowing how to deal with disasters is one of the skills that is needed in the hotel industry. because it is an obligation to make hotels safe from the threat of disasters, there are three types of disasters, natural disasters, non-natural disasters, and social disasters. In this research, researchers will conduct research related to non-natural disasters at the Harris Vertu hotel, namely fire disasters. Reseach metods: This study used qualitative-descriptive research techniques that used observation, interviews, and documentation to collect data. The samples of this study is HR Director, Chief Security, and Chief Engineer. Then the results of the data collected will be described descriptively. Result and discussion: The haris Vertu Hotel Jakarta has prepared the staffs to face disasters from the first day the staff worked at the Harris Vertu hotel where on the first day of work they were immediately given a full day of training, usually the training is given by HRD about hotel brands, executive chefs about hygiene, Chief Security about how to respond to disasters. In addition to the training provided on the first working day of the Harris Vertu hotel, it is also routine to conduct training every three months on how to deal with disasters if at any time they occur at the Harris Vertu hotel, things to do when disasters occur and how to use the tools used to deal with disasters.Harris Vertu Hotel also routinely conducts simulations every four months for all its staff, in the simulation each staff is given the opportunity to use all the equipment used to deal with disasters, besides that Hrris Vertu hotel also collaborates with outside parties to carry out this simulation, such as by ambulance, and fire department. Implication: This study focuses to answer the following questions: 1) How does Harris Vertu Hotel prepare for natural disasters, 2) How does Harris Vertu Hotel prepare its staff to face natural disasters. Keywords: Hotel, Fire, Non-natural Disaster, Disaster Mitigation

A Machine learning approach for Post-Disaster data curation

Image data collected after natural disasters play an important role in the forensics of structure failures. However, curating and managing large amounts of post-disaster imagery data is challenging. In most cases, data users still have to spend much effort to find and sort images from the massive amounts of images archived for past decades in order to study specific types of disasters. This paper proposes a new machine learning based approach for automating the labeling and classification of large volumes of post-natural disaster image data to address this issue. More specifically, the proposed method couples pre-trained computer vision models and a natural language processing model with an ontology tailed to natural disasters to facilitate the search and query of specific types of image data. The resulting process returns each image with five primary labels and similarity scores, representing its content based on the developed word-embedding model. Validation and accuracy assessment of the proposed methodology was conducted with ground-level residential building panoramic images from Hurricane Harvey. The computed primary labels showed a minimum average difference of 13.32% when compared to manually assigned labels. This versatile and adaptable solution offers a practical and valuable solution for automating image labeling and classification tasks, with the potential to be applied to various image classifications and used in different fields and industries. The flexibility of the method means that it can be updated and improved to meet the evolving needs of various domains, making it a valuable asset for future research and development.

Formation mechanism of a disaster chain in Loess Plateau: A case study of the Pucheng County disaster chain on August 10, 2023, in Shaanxi Province, China

Under the heavy rainfall risk due to global warming, a new trend has emerged in geological disasters of loess, which have often evolved into a chain form of disaster chain of loess (DCL) in recent years. The DCL is characterized by multiple, hidden, catastrophic, and complex characteristics that seriously affect the construction and operation of large-scale infrastructure on the Loess Plateau. To understand the formation mechanism of a disaster chain of loess, we took the Shiyangpo DCL, a typical disaster chain occurring recently on the Loess Plateau, as an example to investigate the geomorphic features and deformation characteristics of DCL using new technologies and methods such as Unmanned Aerial Vehicle (UAV) mapping and Geographic Information Systems (GIS) spatial analysis technology. A series of special laboratory tests considering the vibration of the loess subgrade was conducted to explore the changes in physical and mechanical properties of loess samples in the study area under natural, saturated, and vibration conditions. Additionally, the trigger factors and evolution process of this DCL were analyzed, and the formation mechanism of recently emerging typical DCL was revealed as well. The triggering factors of the disaster were summarized as follows: loess nature, heavy rainfall, irrigation, irrational excavation, incomplete drainage channels, and long-term vehicle vibration of roadbeds. Furthermore, extreme rainfall was identified as the primary inducing factor of Shiyangpo DCL. Finally, the development and evolution of Shiyangpo DCL were divided into five stages: the formation of the loess sinkhole stage, the occurrence of the loess subsidence stage, the occurrence of the loess collapse stage, the occurrence of the loess landslide stage, and the formation of river-blocking and dammed lake stage. This study reveals the cause and evolution process of the newly emerged DCL in the Loess Plateau, and the new techniques and methods involved can provide references for the theoretical research and prevention of loess geological disasters in other places.