Disaster Prevention Strategies Research Articles

Application of GIS Spatial Analysis for the Assessment of Storm Surge Inundation Risks in the Guangdong–Macao–Hong Kong Great Bay Area

This study evaluates the storm surge inundation risk in three anthropogenically infilled estuaries—Xichong, Renshan, and Kaozhouyang—located in the Guangdong–Macao–Hong Kong Great Bay Area, China. By integrating GIS spatial analysis with storm surge modeling, we conducted 204 numerical experiments to simulate storm surge inundation under varying typhoon intensities and astronomical tide conditions. Results revealed that coastal terrain plays a crucial role in influencing storm surge levels and inundation extents. Specifically, the pocket-shaped terrain in the Renshan and Kaozhouyang estuaries amplified storm surges, resulting in higher inundation levels compared to the relatively open terrain of Xichong. Furthermore, anthropogenically reclaimed land in these estuaries appear to be particularly vulnerable to storm-induced inundation. Overall, this study underscores the importance of considering coastline morphology and the anthropogenic modifications of coastal terrain in storm surge risk assessments, offering valuable insights for disaster prevention and mitigation strategies. The use of ArcGIS spatial analysis coupled with storm surge modeling, facilitated by high-resolution DEMs, provides a statistical risk assessment of inundation. However, more complex flooding dynamics models need to be developed, particularly when terrestrial bottom friction information, which is heavily modified by human activities, can be accurately incorporated.

Multifactor Mathematical Modeling and Analysis of the Impact of Extreme Climate on Geological Disasters

Objective: To investigate the impact of extreme climate on geological disasters in Shanxi and propose effective disaster prevention and mitigation strategies. Methods: Using daily temperature and precipitation data from 27 meteorological stations in Shanxi Province from 1975 to 2020, 32 extreme climate indices were calculated. Combined with geological disaster site data, the distribution characteristics of extreme climates and their relationship with geological disasters were analyzed, and a regression model for geological disaster risk zones was constructed. Results: Sixteen extreme climate indices in Shanxi Province showed significant changes, especially TMAXmean (100% significant). Indices related to negative precipitation effects showed a declining trend, with 77.78% being significant, while 96.3% of positive temperature effect indices showed an increasing trend, with 73.6% being significant. Geological disaster hotspots were concentrated in the mid-altitude (500–1500 m) hilly and low mountain areas along the central north–south axis and on Q and Pz strata. Extreme high-temperature indices were significantly positively correlated with geological disaster hotspots, while extreme low-temperature indices were negatively correlated. Indices related to extreme heavy precipitation (e.g., R99p.Slope, RX5day.Slope) were associated with an increase in geological disaster hotspots, whereas higher total precipitation and frequent heavy precipitation events were associated with a decrease in disaster hotspots. The grey relational degree between the Z-score and TXn.Slope, TXx.Slope, GSL.Slope, and TX90P.Slope was greater than 0.8. The random forest model performed best in evaluation metrics such as MAE, RMSE, and R2. Conclusions: Shanxi is likely to experience more extreme high-temperature and precipitation events in the future. The low-altitude hilly and terraced areas in Zones III and VII are key regions for geological disaster prevention and control. High temperatures and extreme rainfall events generally increase the disaster risk, while higher total precipitation reduces it. The random forest model is the optimal tool for predicting geological disaster risks in Shanxi Province.

Landslide susceptibility and building exposure assessment using machine learning models and geospatial analysis techniques

Landslides are among the most dangerous hazards in Asia, posing a significant threat to human lives, infrastructure, and sustainable development. Landslide susceptibility maps provide useful insights into hazard potential but lack quantitative exposure assessments to develop targeted mitigation strategies and resource allocation. This study aims to propose an integrated approach for landslide hazards and exposure evaluation using machine learning models on the Google Earth Engine environment and geospatial analysis techniques. A geospatial database was established to predict hazards and evaluate exposure, including data on topography, geology, hydrology, climate features, land use, and building data. The landslide susceptibility map was created with advanced machine-learning algorithms, including Classification And Regression Tree (CART), Random Forest (RF), Gradient Boosting (GB), and Support Vector Machine (SVM). The ROC curve and the Wilcoxon signed-rank test were employed to evaluate the performance differences among the CART, GB, RF, and SVM models. The results indicated that the RF model demonstrated the highest performance, leading to its selection for creating a landslide susceptibility map. Landslide exposure was evaluated by overlaying the landslide susceptibility map with building data to quantify the number of affected houses by landslides across districts and communes. The analysis results identified the Cho Don district as the most exposed, with 46,237 households located in high and very high landslide susceptibility zones, followed by Ba Be district (39,631 households), Bac Kan city (37,266 households), Bach Thong district (28,495 households), Cho Moi district (28,436 households), Na Ri district (17,723 households), Ngan Son district (14,142 households), and Pac Nam district (13,034 households). These findings enable the identification of the potential consequences of landslides on infrastructure, human settlements, and livelihoods, contributing to the promotion of disaster reduction and prevention strategies.

Development of a novel storm surge inundation model framework for efficient prediction

Abstract. Storm surge is a natural process that causes flood disasters in coastal zones and results in massive casualties and property losses. Therefore, storm surge inundation is of major concern in formulating appropriate strategies for disaster prevention and mitigation. However, traditional storm surge hydrodynamic models have large limits with respect to computational efficiency and stability in practical applications. In this study, a novel storm surge inundation model was developed based on a wetting and drying algorithm established from a simplified shallow-water momentum equation. The wetting and drying algorithm was applied to a rectangular grid that iterates through a cellular automata algorithm to improve computational efficiency. The model, referred to as the Hydrodynamical Cellular Automata Flood Model (HCA-FM), was evaluated by comparing the simulations to regional field observations and to a widely used hydrodynamic numerical model. The comparisons demonstrated that HCA-FM can reproduce the observed inundation distributions and predict results that are consistent with the numerical simulation in terms of the inundation extent and submerged depth with much improved computational efficiency (predicting inundation within a few minutes) and high stability. The results reflect significant advancement of HCA-FM toward efficient predictions of storm surge inundation and applications at large spatial scales.

Dynamic evolution characteristics and hazard assessment of compound drought/waterlogging and low temperature events for maize

Hazard assessment is fundamental in the field of disaster risk management. With the increase in global warming, compound water and temperature events have become more frequent. Current research lacks risk assessments of low temperatures and their compound events, necessitating relevant hazard assessment work to improve the accuracy and diversity of maize disaster prevention and mitigation strategies. This study comparatively analyzed the dynamic evolution characteristics and hazards of compound drought/waterlogging and low temperature events (CDLEs and CWLEs) for maize in the Songliao Plain during different growth periods from 1981 to 2020. First, composite drought/waterlogging and low temperature magnitude indices (CDLMI and CWLMI) were constructed to quantify the intensity of CDLEs and CWLEs by fitting non-exceedance probabilities. Next, static and dynamic hazard assessment models were developed by fitting probability density and cumulative probability density curves to CDLMI and CWLMI. The results showed that the correlations between SPRI and LTI across different decades were mainly negative during the three growth periods. The hazard ratings for both CDLEs and CWLEs were relatively high in the northern part of the study area, consistent with the higher occurrence, duration, and severity of both CDLEs and CWLEs at higher latitudes. Relative to 2001–2010, the center of gravity of hazard shifted southward for CDLEs and northward for CWLEs in 2011–2020. The mean duration, frequency, and hazard were generally higher for CWLEs, but CDLEs were associated with more severe maize yield reductions. This study provides new insights into compound disaster risk assessment, and the research methodology can be generalized to other agricultural growing areas to promote sustainable development of agricultural systems and food security.

Exploring sustainable watershed flood risks management: An innovative TFAHP-TOPSIS methodology in the Georges River Basin, Australia

Floods occur frequently in low-lying coastal and riverine areas, making it crucial to effectively identify the distribution of flood risks in flood-prone areas. Traditional risk assessment models based on Multiple-Criteria Decision-Making (MCDM) method have limitations in both accuracy and effectiveness, particularly in identifying potential risks in watershed floodplains. To refine the flood measurement process, an optimized integrated MCDM model is proposed in this paper. Taking the Georges River Basin in Australia is chosen as an example, twelve risk indicators were identified, considering factors related to hazard, exposure, and vulnerability. To more accurately describe the characteristics of watershed floods, the flood hazard indicators were separated into sub-basins. The Triangular Fuzzy Analytic Hierarchy Process (TFAHP) is employed to optimize the weight calculation, and in combination with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), the spatial distribution of flood risks was determined. The results indicate that high-risk areas are concentrated in the central-eastern part of Liverpool, the eastern part of Fairfield, and the central part of Bankstown-Canterbury. Medium to very-high potential risk areas identified by the integrated TFAHP-TOPSIS model are 17.3 % more than the single TFAHP method and 12.7 % more than the TOPSIS method, indicating that the integrated model performs better in identifying potential risks in floodplains. The results can provide decision support for disaster prevention strategies making in flood-prone areas.

Prediction and explanation of debris flow velocity based on multi-strategy fusion Stacking ensemble learning model

The debris flow velocity fundamentally determines its intensity, thereby rendering its prediction a crucial aspect of disaster prevention and control strategies. However, accurate velocity prediction has consistently posed significant challenges due to the intricate interplay of various influential factors. To address the limitations of existing models, an explainable multi-strategy fusion of Stacking ensemble learning is proposed. Initially, an improved snake optimization (ISO) algorithm is employed to adjust parameters within the model’s learners. Benchmark function comparison tests are then conducted to validate the reliability of the ISO algorithm. Subsequently, a learner selection method based on predictive performance and degree of difference is established to facilitate the selection of basic learners and meta-learners. This leads to the construction of the Stacking ensemble learning model, achieved through the integration of parameter optimization strategies from the improved swarm intelligence algorithm strategy, the error weighting strategy, and the decomposition strategy. To assess the model, a case study of the Jiangjiagou Gulley debris flow is undertaken, focusing on the prediction of the debris flow velocity. The results demonstrate high predictive accuracy, with RMSE, MAE, and MAPE values of 0.19, 0.17, and 2.46% respectively. Furthermore, under the SHAP framework, global and local explanations of the predictions are provided. Through feature importance analysis, the bed slope gradient is identified as the most crucial feature in the velocity prediction of the Jiangjiagou Gulley debris flow. Coupling effects and contributions of input features to the debris flow velocity prediction are further analyzed and explained through feature interaction analysis and single sample analysis. This study not only provides a new method for debris flow velocity prediction but also provides guiding suggestions for debris flow monitoring and control.

Snowpack variations and their hazardous effects under climate warming in the central Tianshan Mountains

Climate change alters snowpack evolution, which in turn influences the likelihood of snow avalanches and flood risks. The lack of systemic observational data on key snow characteristics in high mountains remains a scientific challenge in terms of systematically elucidating the dynamic chain of variations in climate–snowpack–snow disasters. This restricts our understanding and poses challenges in the prediction of snow-related disaster risks. As such, this study analysed the variations of temperature and snowfall and the physical characteristics of snowpacks based on ground-based observations from the Kunse River Valley situated in the Tianshan Mountains from 1967 to 2021. The results reveal that the temperature increased significantly by 0.32 °C per decade (p < 0.01) during the snow season, along with more extreme snowfall events. The snow-cover duration was observed to have been shortened by 4.77 d per decade (p < 0.01) from 1967 to 2021, which is characterised by later snow-cover onset and earlier snowmelt. Concurrently, average and maximum snow depths increased along with an increase in peak snow water equivalent, thus indicating a higher frequency of extremely scarce or abundant snow years. The low snowpack temperature gradient and earlier snowmelt dates in spring lead to earlier occurrences of snowmelt floods and wet avalanches. As the risks of these events increase, they pose greater threats to farmlands, road transportation, water–electricity infrastructure and several other human activities. Therefore, these insights are critical for providing vital information that can deepen our understanding of the impact of climate change on snowpack characteristics and improve management strategies for snow-related disaster prevention and mitigation.

Digital twin approach for enhancing urban resilience: A cycle between virtual space and the real world

Construction of disaster-resilient cities has attracted considerable attention. However, traditional methods of studying urban disaster resilience through experimental approaches are often constrained by various limitations, such as testing sites, costs and ethical considerations. To address these constraints, this paper proposes incorporating digital twin concepts into urban disaster resilience research. By establishing a connection between the physical realm of the city and its virtual counterpart, this approach utilizes digital simulations to overcome the limitations of experimental methods and enables dynamic deduction and control of the disaster process. This paper delves into three key aspects encompassing the acquisition of data from reality to the virtual space, disaster simulation within the virtual space, and translation of virtual insights into effective disaster prevention strategies in reality. It provides a comprehensive summary of relevant research endeavors from the authors’ research group and showcases the effectiveness and potential of the proposed techniques. These findings serve as references for pre-disaster planning, real-time emergency assessments, post-disaster rescue operations, and accident investigations for buildings and cities.

Research on Polar Operational Limit Assessment Risk Indexing System for Ships Operating in Seasonal Sea-Ice Covered Waters

The Polar Operational Limit Assessment Risk Indexing System (POLARIS) has been established as a viable framework for assessing operational capabilities and associated risks in polar waters. Despite its inherent suitability for high-latitude territories, ships navigating through seasonal ice-infested waters at lower latitudes also encounter critical safety, environmental, and economic issues exacerbated by the presence of ice. This necessitates a reliable and adaptable methodology that can serve as a reference for devising effective countermeasures. This study evaluated the use of POLARIS in the intricate ice conditions prevalent in the northern navigable waters (channels and anchorages) within Liaodong Bay of the Bohai Sea, located at relatively low latitudes. Using GF-4 satellite imagery, ice conditions were collected, and the POLARIS methodology was employed to calculate Risk Index Outcome (RIO) values for non-ice-strengthened vessels during the winter season of 2021–2022. The results showed that sectors 3, 4, 5, 7, 9, 10, and 11 within the northern part of Liaodong Bay exhibited a higher risk, with sectors 5 and 10 exhibiting the most significant risk, while sectors 1 and 2 demonstrated relatively lower risk levels. The concurrence of these findings with acknowledged ice patterns and local maritime practices confirms the applicability of the POLARIS methodology in saline, seasonally ice-covered seas. Notably, the combination of POLARIS with high-resolution satellite imagery facilitated a more precise and rapid assessment of ice risk, thereby enhancing situational awareness and informing decision-making processes in maritime operations under icy conditions. In addition, this study provides preliminary evidence that POLARIS is suitable for fine-scale scenarios, in addition to being applicable to sparse-scale scenarios, such as polar waters, especially with high-resolution ice data. At the same time, this study highlights the potential of POLARIS as a disaster prevention strategy and a tool for the maritime industry to address ice challenges.

Effect of Mineral Composition and Particle Size on the Failure Characteristics and Mechanisms of Marble in the China Jinping Underground Laboratory.

In deep underground engineering, the deformation, failure characteristics, and mechanism of surrounding rock under the influence of grain sizes and mineral compositions are not clear. Based on CJPL-II variously colored marbles, the differences in grain size and mineral composition of the marble were analyzed by thin-section analysis and XRD tests, and the effect of intermediate principal stress on the mechanical properties of marble was investigated. Both SEM and microfracture analysis were coupled to reveal the failure mechanisms. The results highlight that the crack initiation strength, damage strength, peak strength, and elasticity modulus of Jinping marble exhibit an increasing trend with an increase in intermediate principal stress, while the peak strain initially increases and subsequently decreases. Moreover, this study established negative correlations between marble strength, brittleness characteristics, and fracture angle with grain size, whereas positive correlations were identified with the content of quartz, sodium feldspar, and the magnitude of the intermediate principal stress. The microcrack density in marble was found to increase with larger grain sizes and decrease with elevated quartz and sodium feldspar content, as well as with increasing intermediate principal stress. Notably, as the intermediate principal stress intensifies and grain size diminishes, the transgranular tensile failure of marble becomes more conspicuous. These research findings contribute to the effective implementation of disaster prevention and control strategies.

Snow Disaster Risk Assessment Based on Long-Term Remote Sensing Data: A Case Study of the Qinghai–Tibet Plateau Region in Xizang

The risk analysis and assessment of snow disasters are essential foundational tasks in natural disaster management and profoundly impact the scientific and precise formulation of disaster prevention, preparedness, and mitigation strategies. Employing the theory and methodology of snow disaster assessment, this research focuses on historical and potential snow disasters in the Qinghai–Tibetan Plateau (QTP) Region. Utilizing a long-time-series snow depth remote sensing dataset, we extracted six assessment indicators for historical snow disaster risk factors and potential snow disaster risk factors. We determined the weights of these six assessment indicators using the entropy weight method. Subsequently, we established a snow disaster assessment model to evaluate the grade distribution of snow disasters in the study area. This method can effectively solve the problem of the sparse data distribution of meteorological stations and reflect degrees of snow disaster risk on a large spatial scale. The findings reveal that areas with a relatively high snow disaster risk are primarily concentrated in the western part of the Ali Region, the central part of Chamdo, and near the border in Southern Xizang. Additionally, regions with a high frequency of snow disasters are predominantly located at the junction of Nagchu, Chamdo, and Nyingchi in the eastern part of Xizang. These results contribute valuable insights into the risk assessment of snow disasters and facilitate the development of effective strategies for disaster management in the region.

Variation in Debris-Flow-Prone Areas with Ecosystem Stability: A Case Study of the Qipan Catchment in the Wenchuan Earthquake Region

The spatial distribution of vegetation in a basin has a far-reaching influence on the potential for sediment separation and transport capacity. However, many landslides induced by strong earthquakes have greatly changed the existing pattern, which further increases the probability of debris flow in a basin during heavy rainfall and has a significant impact on the stability of the basin. Thus, this study selected the debris flow basin in the Qipan catchment of the Wenchuan earthquake area as the research object. Multisource and high-precision remote sensing images were used to analyze the land use changes in the basin, and the index of connectivity (IC) was introduced to analyze the evolution of sediment transport capacity. An ecosystem stability assessment method suitable for post-earthquake debris flow basins was proposed. Through quantitative assessment of the ecosystem stability of the basin after the Wenchuan earthquake in 2008 and the two debris flow events after the earthquake, the dynamic relationship between the debris-flow-prone area and the ecosystem stability of the basin was revealed. The results showed that the stability of the ecosystem in the Qipan catchment increased annually, indicating a stable and substable state. The spatial distribution characteristics were lower in the north and south and greater in the middle. By comparing the evaluation results with the actual terrain change trend, the accuracy and feasibility of the evaluation method are verified. The results of this study provide a scientific basis for the formulation of regional disaster prevention strategies and help to accelerate the improvement of regional stability in debris-flow-prone areas.

Physical model test on rainfall-induced instability of silty slope

In the context of ecological conservation and high-quality development in the Yellow River Basin, extensive silty slopes have emerged in the downstream alluvial plain regions owing to engineering construction. These slopes are susceptible to rainfall infiltration-induced instability, resulting in geological hazards, such as landslides and mudslides. This study explored the hydrodynamic characteristics and spatiotemporal evolution patterns of silty slopes under rainfall conditions, focusing on the alluvial silty slopes of the Yellow River. Experiments were performed to monitor variations in soil water content, soil water potential, and pore water pressure at various locations on the slope. Slope instability and failure under rainfall were monitored using high-frequency photography technology. The results indicated the development of layered vertical collapses along the surface of the slope, starting from the base and accumulating below the failure point. With increasing rainfall intensity, soil water content, soil water potential, and pore water pressure increased earlier at various points on the slope. These findings provide crucial experimental data and a theoretical foundation for exploring the instability mechanisms within silty slopes in the lower Yellow River’s alluvial and siltation plain areas during rainfall events. Moreover, they are significant for formulating more effective strategies for geological disaster warning and prevention.

GIS-Based assessment of geological hazard susceptibility in Southeastern Tibetan region: An information entropy approach

The terrain and geological conditions in southeastern Tibetan region are complex, especially under the influence of hydraulic and hydroelectric development, and susceptibility to geological hazards has increased. This study focused on three counties in Changdu City. We aimed to assess the susceptibility to geological hazards in this region using Geographic Information System (GIS) technology and information entropy models. Historical geological hazard data from the local area, remote sensing data, and GIS were utilized to develop an information entropy model for landslide geological hazard analysis. Key triggering factors for landslide geological hazards in the southeastern Tibetan region were identified, and a comprehensive dataset was compiled. Information entropy model was applied to consider various influencing factors and calculate their impact on geological hazards, Landslide risk in the area was analyzed and generate a susceptibility map for geological hazards. Our results indicate significant correlations between factors, such as steep terrain, human engineering activities, and land types with landslide occurrences. The established evaluation model can reasonably predict potential geological hazard risk in Nujiang River region and partially reflect the distribution characteristics of geological hazards in the southeastern Tibetan region. This offers a scientific foundation for relevant authorities to formulate disaster prevention and response strategies.

Study on agricultural drought disaster risk assessment in Heilongjiang reclamation area based on SSAPSO optimization projection pursuit model.

Heilongjiang reclamation area serves as a crucial hub for commodity grain production and strategic reserves in China, playing a vital role in maintaining national food security. Investigating the assessment of agricultural drought risk in this region can yield valuable insights into spatial and temporal variations in drought risk. Such insights can aid in formulating effective strategies for disaster prevention and mitigation, thereby minimizing food losses caused by drought disasters. This study employs a comprehensive indicator system comprising 17 indicators categorized into hazard, exposure, vulnerability, and resistance capacity. The projection pursuit model is applied to evaluate regional drought risk, while the PSO algorithm, optimized by the SSA algorithm, addresses the limitations of low local search ability and search accuracy during the large-scale search process of the PSO optimization algorithm. This study examines and compares the optimization and convergence capabilities of three algorithms: real number encoding-based genetic algorithm (RAGA), particle swarm optimization algorithm (PSO), and sparrow algorithm-based improved particle swarm optimization algorithm (SSAPSO). The analysis demonstrates that SSAPSO exhibits superior optimization performance and convergence properties, establishing it as a highly effective algorithm for optimization tasks. The findings reveal the following trends: over time, agricultural drought risk in Heilongjiang reclamation area has generally declined, with fluctuations observed in hazard and vulnerability, an increase in exposure, and a continuous enhancement of resistance capacity. Spatially, the western region exhibits significantly higher agricultural drought risk compared to the eastern region, primarily due to elevated hazard and vulnerability, coupled with lower resistance capacity. As the agricultural economy grows and agricultural expertise accumulates, the risk of agricultural drought decreases. However, variations in economic growth among different regions lead to diverse spatial distributions of risk.

Landslide susceptibility assessment along the Karakoram highway, Gilgit Baltistan, Pakistan: A comparative study between ensemble and neighbor-based machine learning algorithms

This study addressed the complex challenges associated with landslide detection along the Karakoram Highway (KKH), where tectonic events and data availability limitations posed significant obstacles. To overcome these hurdles, the research framework encompassed several critical components. First, it tackled the issue of multicollinearity through the application of statistical measures such as Variable Inflation Factor (VIF) and Information Gain (IG). Secondly, the study emphasized the importance of selecting a study area that would comprehensively represent the multivariate landscape, with KKH serving as an illustrative example. In striving for an equilibrium between implementation ease and algorithmic performance, the research favored the adoption of Random Forest (RF) and Extremely Randomized Trees (EXT) over XGBoost. Lastly, to fine-tune the algorithms and optimize their parameters, the study employed Particle Swarm Optimization (PSO) and evaluated their performance using metrics like the Area Under the Curve (AUC). Remarkably, this comprehensive approach yielded accuracy rates exceeding 90% for all algorithms tested (RF, EXT, and K-Nearest Neighbor (KNN)), with specific AUC values of 0.967, 0.968, and 0.914, respectively. These findings offer invaluable insights into enhancing disaster prevention strategies and informing land-use planning efforts along the KKH highway.

Developmental characteristics of loess mudflow and correlation analysis with influencing factors

Loess mudflow is a unique geological hazard in the Loess Plateau, presenting a significant risk to the lives and properties of the region’s inhabitants. Defining and categorizing loess mudflow scientifically, as well as comprehending their disaster-prone environment and patterns of development, are crucial for implementing effective disaster prevention and mitigation strategies. Based on extensive field investigations and statistical analyses, this study defines and categorizes the loess mudflow into five types: liquefaction flow, freeze-thaw creep, subsurface erosion failure, collapse-mudflow, and erosion-enlargement. Statistical analysis reveals that the drainage area of loess mudflow is mostly concentrated between 0.15 and 30 km2, the bed slope typically ranges from 10‰–200‰, material storage in channels is mostly above 5 × 104 m3/km2, and the rainfall intensity triggering mudflow formation is generally greater than 10 mm/h; furthermore, a correlation analysis between the parameters of loess mudflow and the influencing factors reveals a significant linear growth relationship between mudflow volume and drainage area, and also reveals a significant power function relationship with the increase in channel bed slope. However, there is no significant correlation between mudflow volume and solid material storage, and mudflow density shows no significant correlation with drainage area, channel bed slope, or solid material storage. Finally, an empirical formula for estimating mudflow volume is proposed through regression analysis. The research findings aim to provide a theoretical basis for the study and risk assessment of loess mudflow disasters.

The disaster of the century, Kahramanmaras earthquake: A qualitative study on the experiences of the earthquake victims and search and rescue teams

This study aimed to explore the experiences of earthquake victims who experienced the Kahramanmaras earthquakes and individuals who actively served in search and rescue teams in the field. In this qualitative study, data were collected through semi-structured, in-depth interviews and focus group discussions (FGDs) forms from 47 earthquake victims living in various tent cities and 46 members of search and rescue teams in the Kahramanmaras and Hatay provinces. In-depth individual and focus group interviews were conducted using the maximum variation sampling method, which is a purposive sampling method, to identify the experiences of the participants. Data were analyzed using the content analysis method. The results revealed that search and rescue teams faced problems in coordination and transportation, communication, debris safety, and multidisciplinary reinforcement of their teams, especially during the intervention phase. Earthquake victims, on the other hand, were affected psychosocially and faced many problems such as access to the allocation of resources and aids and lack of information before, during, and after the earthquakes. In this context, these themes on the experiences of large-scale earthquakes that caused great psychosocial, economic, and social devastation from the perspectives of earthquake victims and search and rescue teams can contribute to the planning of early disaster prevention and response strategies for policymakers to improve disaster management at national and international levels.

Towards Resilient Cities: Optimizing Shelter Site Selection and Disaster Prevention Life Circle Construction Using GIS and Supply-Demand Considerations

City health examinations are integral to China’s urban planning, construction, and management. They effectively identify potential risks and vulnerabilities in urban development, ensuring safety resilience—a critical component. This resilience enhances the city’s ability to withstand internal and external shocks, promoting the safety of urban residents and fostering sustainable city development. Drawing on the Japanese disaster prevention strategy, the disaster prevention life circle emerges as a rescue and protection system during urban disasters, fortifying urban safety resilience. However, smaller and mid-sized cities, constrained by limited resources, significantly need to catch up in disaster prevention planning. Consequently, bolstering safety resilience in these cities becomes a pressing concern. This study focuses on Lindian County in Heilongjiang Province as the urban area under consideration for resilient city objectives. Leveraging the ArcGIS network analysis tool, we optimize the placement of emergency shelters, aligning with urban disaster assessments and the equilibrium of disaster prevention facility supply and demand. Accessibility analysis of emergency shelters was conducted using the Gaussian two-step floating catchment area method. Ultimately, we integrate the range of demand points assigned to each shelter, along with the effective land area reflecting the supply of shelters, as weights into a weighted Voronoi diagram. This diagram is combined with a reference to the entire region to delineate the disaster prevention life circle. Findings reveal that, under the premise of minimizing government construction costs while maximizing coverage and evacuation utilization rates, the optimal resident emergency congregate shelters in the study area are 8, with 98 emergency evacuation and embarkation shelters. Striking a balance between disaster prevention facility supply and demand and regional accessibility, the urban area of Lindian County is segmented into 3 resident disaster prevention life circles and 24 emergency disaster prevention life circles. The objective of this study is to optimize shelter siting and establish disaster prevention life circles in diverse urban areas. This endeavor aims to bolster urban resilience and foster sustainable urban development.