Disaster Prevention Research Articles

How Severe Was the 2022 Flash Drought in the Yangtze River Basin?

Flash droughts, characterized by their rapid onset and severe impacts, have critical implications for the ecological environment and water resource security. However, inconsistent definitions of flash droughts have hindered scientific assessments of drought severity, limiting efforts in disaster prevention and mitigation. In this study, we propose a new method for explicitly characterizing flash drought events, with particular emphasis on the process of soil moisture recovery. The temporal and spatial evolution of flash droughts over the Yangtze River Basin was analyzed, and the severity of the extreme flash drought in 2022 was assessed by comparing its characteristics and impacts with those of three typical dry years. Additionally, the driving factors of the 2022 flash drought were evaluated from multiple perspectives. Results indicate that the new identification method for flash droughts is reasonable and reliable. In recent years, the frequency and duration of flash droughts have significantly increased, with the Dongting Lake and Poyang Lake basins being particularly affected. Spring and summer were identified as peak seasons for flash droughts, with the middle reaches most affected in spring, while summer droughts tend to impact the entire basin. Compared to 2006, 2011, and 2013, the flash drought in 2022 affected the largest area, with the highest number of grids experiencing two flash drought events and a development rate exceeding 15%. Moreover, the summer heat in 2022 was more extreme than in the other three years, extending from spring to fall, especially during July–August. Its evolution was driven by the Western Pacific Subtropical High, which suppressed precipitation and elevated temperatures. The divergence of water vapor flux intensified water shortages, while anomalies in latent and sensible heat fluxes increased surface evaporation and heat transfer, further disturbing the regional water cycle. This study provides valuable insights for flash drought monitoring and early warning in the context of a changing climate.

A Review of Cutting-Edge Sensor Technologies for Improved Flood Monitoring and Damage Assessment

Floods are the most destructive, widespread, and frequent natural hazards. The extent of flood events is accelerating in the context of climate change, where flood management and disaster mitigation remain important long-term issues. Different studies have been utilizing data and images from various types of sensors for mapping, assessment, forecasting, early warning, rescue, and other disaster prevention and mitigation activities before, during, and after floods, including flash floods, coastal floods, and urban floods. These monitoring processes evolved from early ground-based observations relying on in situ sensors to high-precision, high-resolution, and high-coverage monitoring by airborne and remote sensing sensors. In this study, we have analyzed the different kinds of sensors from the literature review, case studies, and other methods to explore the development history of flood sensors and the driving role of floods in different countries. It is found that there is a trend towards the integration of flood sensors with artificial intelligence, and their state-of-the-art determines the effectiveness of local flood management to a large extent. This study helps to improve the efficiency of flood monitoring advancement and flood responses as it explores the different types of sensors and their effectiveness.

Construction of Freezing Injury Grade Index for Nanfeng Tangerine Plants Based on Physiological and Biochemical Parameters

Low-temperature freezing stress constitutes the most significant meteorological disaster during the overwintering period in the Nanfeng Tangerine (NT) production area, severely impacting the normal growth and development of the plants. Currently, the accuracy of meteorological disaster warnings and forecasts for NT orchards remains suboptimal, primarily due to the absence of quantitative meteorological indicators for low-temperature freezing stress. Therefore, this study employed NT plants as experimental subjects and conducted controlled treatment experiments under varying intensities of low-temperature freezing stress (0 °C, −2 °C, −5 °C, −7 °C, and −9 °C) and durations (1 h, 4 h, and 7 h). Subsequently, physiological and biochemical parameters were measured, including photosynthetic parameters, chlorophyll fluorescence parameters, reactive oxygen species, osmoregulatory substances, and antioxidant enzyme activities in NT plants. The results demonstrated that low-temperature freezing stress adversely affected the photosynthetic system of NT plants, disrupted the dynamic equilibrium of the antioxidant system, and compromised cellular stability. The severity of freezing damage increased with decreasing temperature and prolonged exposure. Chlorophyll (a/b) ratio (Chl (a/b)), maximum quantum yield of photosystem II (Fv/Fm), soluble sugar, and malondialdehyde (MDA) were identified as key indicators for assessing physiological and biochemical changes in NT plants. Utilizing these four parameters, a comprehensive score (CS) model of freezing damage was developed to quantitatively evaluate the growth status of NT plants across varying low-temperature freezing damage gradients and durations. Subsequently, the freezing damage grade index for NT plants during the overwintering period was established. Specifically, Level 1 for CS ≤ −0.50, Level 2 for −0.5 < CS ≤ 0, Level 3 for 0 < CS ≤ 0.5, and Level 4 for 0.5 < CS. The research results provide valuable data for agricultural meteorological departments to carry out disaster monitoring, early warning, and prevention and control.

A Spatial Landslide Risk Assessment Based on Hazard, Vulnerability, Exposure, and Adaptive Capacity

Landslides threaten human life, property, and vital infrastructure in most mountainous regions. As climate change intensifies extreme weather patterns, the landslide risk is likely to increase, resulting in challenges for disaster management, sustainability development, and community resilience. This study presents a comprehensive framework for assessing landslide risk, integrating advanced machine learning models with the Iyengar–Sudarshan method. Our case study is Son La province, the Northwest region of Vietnam, with data collected from 1771 historical landslide occurrences and fifteen influencing factors for developing landslide susceptibility maps using advanced ensemble machine learning models. The Iyengar–Sudarshan method was applied to determine the weights for landslide exposure, vulnerability, and adaptive capacity indicators. The resulting landslide risk map shows that the highest-risk districts in Son La province are located in the central and northeastern regions, including Mai Son, Phu Yen, Thuan Chau, Yen Chau, Song Ma, and Bac Yen. These districts experience high landslide hazards, exposure, and vulnerability, often affecting densely populated urban and village areas with vulnerable populations, such as young children, the elderly, and working-age women. In contrast, due to minimal exposure, Quynh Nhai and Muong La districts have lower landslide risks. Despite having high exposure and vulnerability, Son La City is situated in a low-susceptibility zone with high adaptive capacity, resulting in a low landslide risk for this region. The proposed framework provides a reference tool for mitigating risk and enhancing strategic decision making in areas susceptible to landslides while advancing our understanding of landslide dynamics and fostering community resilience and long-term disaster prevention.

Spatiotemporal Relationship Between Land Subsidence and Ecological Environmental Quality in Shenfu Mining Area, Loess Plateau, China

The exploitation of coal resources has caused problems such as ground deformation, affecting the ecological environment. Spatiotemporal varying characteristics between land subsidence and ecological environmental quality (EEQ) are an important research hotspot. Using the SBAS-InSAR method, 64 Sentinel-1 images were utilized to monitor land subsidence in the Shenfu mining area, one of China’s largest coal source regions. And the remote sensing ecological index (RSEI) was used to monitor and evaluate EEQ of the Shenfu mining area. Global and local spatial autocorrelation methods were used to assess the spatial aggregation degree and change patterns over time. Spatial Econometric Models were employed to explore the impacts of land subsidence on EEQ. The results showed the following: (1) The average RSEI values in the Shenfu mining area were 0.531, 0.488, and 0.523 in 2016, 2018, and 2020, respectively; there was a slight downward trend in EEQ. The permanent scatter (PS) point deformation rate ranged from −353.40 mm/year to +246.24 mm/year, with average deformation rates of 0.1642, 0.2181, and 0.2490 mm/year, respectively. (2) There was a significant correlation and spatial agglomeration effect between land surface subsidence and EEQ. Low–high, high–low, and low–low clusters were the main types of relationships, indicating that land subsidence primarily has a negative spatial impact on the ecological environment. (3) The relationship between land subsidence and EEQ varied spatially in the Shenfu mining area at 500 × 500 grid units. This research can provide scientific guidance for disaster prevention and sustainable development in mining areas by considering long-term differences in ecological environmental quality and its correlation with land subsidence.

How to encourage more public participation in marine disaster risk management: Evidence from a survey experiment in China

Raising public awareness of maritime risk and disseminating information about disaster prevention and reduction are the most frequent ways that the government incorporates citizens in marine disaster risk management (DRM). However, these measures are deemed to be insufficient to drive the participation rate. This study aims to understand the participation trend of citizens in marine DRM. On the basis of the theory of citizen participation’s ladder, public participation within marine DRM is categorized into non-participation, tokenistic participation, and substantive participation. Using organization theory, the government’s strategies for encouraging participation are classified into common approach (raising awareness), structural approach (innovating instruments), and cultural approach (developing citizenship). Considering the vignette experiment of 403 citizens in a coastal city of China that has historically been subject to marine disasters, it was found that effectiveness of the strategies, from highest to lowest, are citizenship development, risk education, and instruments innovation. At the individual level, psychological characteristics such as trust in the government, past disaster experience, and knowledge of marine DRM did not significantly influence citizens’ participation preferences. At the government level, even when citizens are informed about new participatory mechanisms and tools, they still tend to be unwilling to share responsibilities. However, self-efficacy and understanding the beneficial outcomes of their participation in marine (DRM) can positively impact the willingness to participate. The results show that to encourage public participation substantively in the marine DRM, it is important to cultivate a sense of civic duty and enhance citizens’ sense of ownership, fostering a closer and more equitable partnership between the state and society.

Research and application of hydraulic fracturing axial roof cutting technology for gently inclined hard roof based on abrasive jet

In order to explore a new method and mode of gently inclined hard roof treatment, the hydraulic fracturing axial roof cutting technology based on abrasive jet is introduced, and the key technical parameters of abrasive jet axial cutting and fracturing are determined based on indoor experiments and field industrial experiments. Taking the mining of working face under the condition of hard roof in Kuangou Coal Mine as the background, the implementation process and the effect of mining process are tested by means of water pressure gauge, drilling peep and observation well water level observation, mi-croseismic monitoring, support fracture monitoring and coal stress monitoring. The research results show that the key technical parameters of slotting and fracturing are mastered in the axial cutting test of abrasive jet. Wherein the kerf depth is 200 m, the kerf length is 300 m, the kerf pressure is 40–50 mpa, the fracturing pressure is 50–55 mpa, and the fracturing time is 20–30 min. After grooving fracturing, the cracks in the roof strata are effectively generated and expanded, which destroys the integrity of the roof, and the fracturing radius is 10–20 m. During the mining period, compared with the tradi-tional blasting technology, the concentrated area of microseismic events was shifted from 80 m in front of the working face to 130 m after the combined treatment of abrasive jet axial roof cutting and blasting, and the microseismic energy release was mainly small energy events. After the application of abrasive jet axial roof cutting and scour prevention technology in hard roof, the periodic weighting step is obviously reduced, the influence range of mining stress and stress concentration coefficient are obviously reduced, the activity intensity and dynamic load effect of surrounding rock are obviously weakened. The research results provide a basis for effective prevention and control of rockburst disasters under the condition of hard roof.

On the Prevention and Control of Geological Disasters and the Utilization of Geological Environment

With the rapid development of the social economy, people's awareness of environmental protection is gradually increasing. This is because the probability of geographical disasters occurring in the country is also gradually increasing. People have also begun to pay attention to the prevention of geological disasters and the geological environment, because geological disasters can have a great impact on people's life safety at any time. Therefore, it is necessary to take corresponding preventive measures in advance for geological disasters. This article conducts a series of analyses on geological disasters and other related topics.

Experimental Investigation on Rock Failure Characteristics of Large-Span Goafs Using Digital Image Correlation Analysis and Acoustic Emission Monitoring

Rockmass in deep mining is highly susceptible to large-scale collapses under high stress and blast-induced disturbances, leading to casualties and economic losses. To investigate the evolution characteristics of goaf instability and the types of seismic sources that induce instability, an experiment on goaf instability was designed under uniaxial compression conditions based on actual mining operations. The entire experimental process was monitored using digital image correlation analysis and acoustic emission monitoring. By calculating the digital speckle field on the surface of the rock specimen during the experiment, the evolution characteristics of the deformation and strain fields from the beginning of loading to complete failure were analyzed. The study explored the dynamic behavior of cracks from initiation to propagation and eventually inducing large-scale collapse. The results show that the instability process of the goaf begins with the formation of tensile cracks. As stress increases, shear cracks occur in the specimen, leading to macroscopic failure. Furthermore, based on the differences in overall microfracture types measured by RA-AF characteristic parameters during specimen failure, large amplitude acoustic emission events corresponding to the formation of dominant macroscopic cracks were selected, and the focal mechanisms of these events were inverted. The results indicate that shear failure sources are significantly more prevalent than tensile failure sources in acoustic emission events leading to goaf instability. These findings can provide useful guidance for the support design and the prevention and control of rockmass instability disasters.

Monitoring of Surface Deformation in the Xi'an Coal Mine, Liaoyuan, Based on Time-Series InSAR

In this study, we employed SBAS-InSAR and Stacking-InSAR methods to monitor and analyze the surface deformation of coal mines in the Xi'an District of Liaoyuan. The Pearson correlation coefficient of the deformation results obtained from the two methods is 0.9. Moreover, the deformation regions monitored by the two techniques exhibit a high degree of consistency in their spatial distribution, validating the accuracy of our monitoring results. Using the monitoring data derived from InSAR and field investigations, we revealed the spatial distribution characteristics of surface subsidence and its temporal evolution between 2018 and 2021. Our findings indicate that subsidence predominantly occurred in five regions, with subsidence velocity slowing after October 2020, suggesting a gradual weakening of surface deformation activities. Additionally, we analyzed surface deformation indicators, such as tilt and curvature, revealing the complexity of the deformation areas characterized by multiple tilt and curvature deformation centers. These results provide a crucial scientific basis for geological disaster prevention and ecological restoration in the mining area, aiding the development of more effective management and restoration strategies.

Generating a Landslide Susceptibility Map Using Integrated Meta-Heuristic Optimization and Machine Learning Models

A landslide susceptibility assessment is one of the critical steps in planning for landslide disaster prevention. Advanced machine learning methods can be used as data-driven approaches for landslide susceptibility zonation with several landslide conditioning factors. Despite there being a number of studies on landslide susceptibility assessment, the literature is limited in several contexts, such as parameter optimization, an examination of the factors in detail, and study area. This study addresses these lacks in the literature and aims to develop a landslide susceptibility map of Kentucky, US. Four machine learning methods, namely artificial neural network (ANN), k-nearest neighbor (KNN), support vector machine (SVM), and stochastic gradient boosting (SGB), were used to train the dataset comprising sixteen landslide conditioning factors after pre-processing the data in terms of data encoding, data scaling, and dimension reduction. The hyperparameters of the machine learning methods were optimized using a state-of-the-art artificial bee colony (ABC) algorithm. The permutation importance and Shapley additive explanations (SHAP) methods were employed to reduce the dimension of the dataset and examine the contributions of each landslide conditioning factor to the output variable, respectively. The findings show that the ABC-SGB hybrid model achieved the highest prediction performance. The SHAP summary plot developed using the ABC-SGB model shows that intense precipitation, distance to faults, and slope were the most significant factors affecting landslide susceptibility. The SHAP analysis further underlines that increases in intense precipitation, distance to faults, and slope are associated with an increase in the probability of landslide incidents. The findings attained in this study can be used by decision makers to develop the most effective resource allocation plan for preventing landslides and minimizing related damages.

Temporal-spatial characteristics of climate drought and its effects on grain yield in Hunan Province, China

The examination of the spatiotemporal characteristics and developmental trends of drought is crucial for enhancing water resource management, bolstering drought resistance, and improving disaster prevention capabilities. This study employs the Standardized Precipitation Evapotranspiration Index (SPEI) and grain yield data across various time scales, in conjunction with methodologies such as Run Theory, Mann-Kendall, and Standardized Yield Residual Series, to conduct an in-depth investigation into the spatiotemporal variation characteristics of meteorological drought in Hunan Province and its impact on grain yield. The findings suggest that: (1) Since 1960, the likelihood of seasonal drought occurrence in Hunan Province has been ranked as autumn > winter > spring > summer, with mild drought occurring most frequently, followed by moderate drought, while the frequency of severe and extreme drought remains low. (2) Meteorological drought in Hunan Province exhibits spatial differences at the seasonal scale, with the overall drought changes in spring and summer displaying a non-significant upward trend; the western and southern regions exhibit a trend of aridification in autumn; and in winter, the Zhangjiajie and Xiangxi regions show an insignificant downward trend. (3) From 1960 to 2022, grain production in Hunan Province has demonstrated a pattern of fluctuation and increase. The meteorological yield of grain crops displays a high-low-high spatial distribution from south to north. Concurrently, there is a positive correlation between short-term climate change and meteorological output, while long-term climate change is not evident. (4) El Niño Southern Oscillation (ENSO) is a significant circulation factor affecting meteorological drought in Hunan Province, and the meteorological drought in autumn and winter in Hunan Province is significantly influenced by ENSO. The research findings can provide reference significance and a scientific basis for drought research and comprehensive management in Hunan Province, and offer data and theoretical support for promoting economic development.

Relationship network of safety management elements in the construction industry under the perspective of resilience

PurposeConstruction safety resilience is gradually gaining attention in the field of engineering construction as a new management concept and way to improve safety performance. However, how to cope with the dilemma of the unclear relationship of construction safety resilience elements at the practice level and promote the harmonization of construction safety goals and resilience enhancement paths has become an urgent challenge for safe construction.Design/methodology/approachThis study analyzes the components of construction safety resilience elements. A relationship network model of construction safety resilience elements is developed by using the social network analysis method. The location and influence of each element in the network and the interrelationships among the elements are explored in depth.FindingsThe findings reveal a robust interconnection among the elements of safety resilience in the construction industry. Key components such as safety behavior, risk prevention and control mechanisms, disaster prevention and mitigation technologies as well as information technology, are positioned at the core of the network. Notably, safety behavior exerts the most significant influence over the other elements, serving as the linchpin of safety management in the construction industry. Moreover, the interplay among safety resilience elements in the construction sector can alter the structure of the relationship network.Originality/valueThis study adopts the social network approach to solve the problem that it is difficult to quantitatively analyze the elements of construction safety resilience and their interrelationships and to clarify the interactions among the core elements, which can help to further assist the construction project manager to continuously optimize safety resilience and improve construction safety.

Research on the distribution of debris flow impact on the upstream surface of the check dam

The impact force of debris flow is not only an important indicator of the risk assessment of debris flow and the strength impact resistance of buildings against debris flow, but also an important parameter in the design of various debris flow prevention projects (such as the check dam and the drainage channel, etc.). The pressure sensors are arranged at different positions (monitoring points) on the upstream face of the check dam. By changing the slope of the drainage channel, the bulk density of debris flow and the slope gradient of the upstream face of the check dam, the time history curves of the impact force at the monitoring points under different experiment conditions are obtained. The characteristic value of the impact force of debris flow acting on the surface of the sand retaining dam is analyzed, and the evolution law of mean value and maximum value of impact force of debris flow at the same detection location with the above conditions is obtained. The mean value and maximum value of debris flow impact force at different detection locations under the same working condition are analyzed to obtain the evolution law of debris flow impact force at different locations, and then the distribution trend of debris flow impact force on the upstream face of the check dam is obtained. The research results provide scientific and reasonable theoretical basis and technical support for the stability analysis of the check dam, so as to better serve the disaster prevention and reduction of debris flow, which will improve the technical level of the debris flow prevention project to a certain extent.

A fire risk pre-warning framework for high-rise buildings based on unascertained method.

The growing global interest in preventing and controlling fires in high-rise buildings reflects the increasing significance of this issue today. This research aims to establish an early warning framework for fire risk in high-rise buildings. Firstly, considering the importance of a scientific indicator system for the application of the model, this study combines the event analysis method with the building design fire code to identify 11 key risk factors that have a far-reaching impact on the prevention of fires in high-rise buildings. Based on identifying the risk factors, a high-rise building fire risk warning tree is also established, which scientifically solves the problem of the indicator system of the warning object. Subsequently, in response to the various complex issues arising from the uncertainty of fire occurrence in high-rise buildings, this study adopts the unascertained method to model the fire risk of high-rise buildings for early warning. In addition, the developed methodology was empirically validated through case studies and analyses of empirical data on fire risks in nine representative high-rise buildings. The results of the unascertained method were also compared with the results of the K-means method, from which it was concluded that the unascertained method can predict building fires more accurately. The research results provide a reliable decision support system for fire disaster prevention and control in high-rise buildings.

Study on the evolution rules of coal deformation and failure characteristics caused by multiple mining disturbances

During coal mining operations, the coal will be deformed and damaged due to multiple mining disturbances (MMD), often resulting in disasters, like rock burst. To understand the evolution rules of coal deformation under MMD and its final fracture characteristics after impact dynamic load loading, reduce the adverse effects of mining disturbances, and improve disaster prevention and control capabilities, quasi-static uniaxial cyclic loading-unloading (L-U) and dynamic axial compression tests were conducted on large-sized coal-like samples. During the tests, three-dimensional (3D) laser scanning and acoustic emission (AE) monitoring technology were utilized to accurately capture the full-field deformation and AE response data, facilitating a systematic analysis of deformation and fracture characteristics. The results show that: (1) Under the cyclic L-U effect induced by MMD, each loading cycle causes compression deformation with partial recovery during unloading, presenting an overall “wavy” variation trend. (2) The maximum load is the most critical factor affecting the damaged coal deformation, with smaller load resulting in less overall sample deformation. (3) After the impact dynamic loading, the damaged samples suffered large-scale impact splitting failure, with the compressive-shear layer failure mainly occurred inside the holes. (4) Lower loading during cyclic L-U process correlate with reduced damage degree, and smaller debris particles with a higher fractal dimension when impact failure occurs, indicating a more severe impact failure. (5) With multiple cycles of L-U, the cracks inside the sample gradually extend and expand from around the hole to the outside. The greater the load and the number of cycles, the more serious the crack damage will be. (6) In the practical mining process, it is crucial to reinforce roadway interiors while minimizing low-loading cyclic disturbances induced by MMD. The study has obtained the deformation evolution rules and failure characteristics of coal under MMD, providing a theoretical basis for the prevention and control of corresponding engineering disasters.

Integrating Science, Technology, Engineering, and Mathematics (STEM) into Indigenous Education for Sustainability: The Development and Implementation of a Curriculum Based on Disaster Prevention for Young Children

There are differences between Western mainstream culture and traditional Indigenous culture in the way they address sustainable development. The spirit of sustainability has been emphasized and practiced by Indigenous cultures for hundreds or even thousands of years, but it is increasingly disappearing over time due to the threat of natural disasters. It is necessary to recover this practice of sustainable development from its roots in traditional Indigenous knowledge. This study considers the possibility and utility of incorporating science, technology, engineering, and mathematics (STEM) into Indigenous education for sustainability, a topic that has not been addressed by other studies. Based on a literature review, the proposed framework and content for this study focus on Indigenous disaster prevention. The specific topic was chosen to be most relevant to young Indigenous children. STEM indicators from the US next-generation science standards (NGSS) were referenced to create the proposed STEM teaching objectives, which were designed to be specifically appropriate for Indigenous curricula and teaching activities. Additionally, the cultural curriculum model was adopted to reform the Indigenous curriculum and teaching model by utilizing the transformation and social action approaches. Finally, the five-stage learning cycle was used as the framework to implement the curriculum, intertwined with the principles of the spiral curriculum, to co-construct an instructional example of Indigenous education for sustainability for future reference.

Measurement and impact analysis of social vulnerability to geological disasters in Guizhou Province, China

Investigating social vulnerability is a crucial aspect of attaining an in-depth understanding of disasters and disaster risk. This study developed a measurement framework for social vulnerability to geological disasters, obtained a comprehensive social vulnerability index, and compared the spatial distribution patterns of fatalities, social vulnerability, and geological disaster-prone zones. These efforts assisted in determining the strength and character of the relationship between geological disaster fatality risk and social vulnerability at the county level. The findings show that: (1) the social vulnerability to geological disasters index varies significantly between counties. High social vulnerability is primarily concentrated in Bijie City, Guizhou province, which shows concentrated clustering. Conversely, low social vulnerability is mainly concentrated in Guiyang City: (2) the spatial distribution patterns reveal the limitations of disaster exposure in explaining fatality risk and demonstrate the ability of social vulnerability to amplify disaster risk; (3) the zero-inflated negative binomial regression model fit the data significantly better than the negative binomial regression model, suggesting that for every one unit increase in the social vulnerability index, there is a 6.33% increase in the risk of fatalities, holding all other variables constant. This study establishes a specific framework for measuring social vulnerability to geological disasters and provides new evidence of how social vulnerability influences disaster risk intensity. The county-level spatial measurement results can further assist disaster management personnel in formulating more localized mitigation strategies. The findings will further support the identification of core remediation sites and the implementation of management and relocation (avoidance) projects during Guizhou Province’s 14th Five-Year Plan for geological disaster prevention and control.

Study on Risk Assessment of Rainstorm Disaster in Shanxi Province based on GIS

This study focuses on Shanxi Province, establishing an index system for assessing rainstorm disaster risk based on four factors: risk, vulnerability, sensitivity, and disaster prevention capabilities. The evaluation employs the analytic hierarchy process and a weighted comprehensive assessment method. The findings indicate that certain areas in Taiyuan City and Hunyuan County are classified as high-risk zones, while Yangquan City and the western part of Luliang City are deemed medium-risk areas. Most regions in Shuozhou City, Xinzhou City, Changzhi City, Jincheng City, and Jinzhong City fall into the low-risk category.

Study on The Response Characteristics of Monopile Offshore Wind Turbines under Non-Gaussian loads in Typhoons

The fan structure is often seriously damaged under typhoon, so the study of the fan structure under typhoon is of great significance for its disaster prevention and mitigation. The actual measurement shows that the typhoon has obvious non-Gaussian characteristics, and the influence mechanism of this characteristic on the fan response characteristics is not clear. Therefore study the response characteristics of fans under non-Gaussian load. First, based on the autoregressive AR model, combined with the Johnson conversion model (JTM); second, the wind load is determined based on the specification; then the wind load. Finally, the response characteristics of 5MW fan are studied by characterizing the numerical cases of non-Gaussian fan with different strength of typhoon. The results show that the non-high characteristics of non-Gaussian wind field and the non-Gaussian characteristics become stronger with the strength of the non-Gaussian characteristics of wind field.