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The evaluation of slope stability is of crucial importance in geotechnical engineering and has significant implications for infrastructure safety, natural hazard mitigation, and environmental protection. This study aimed to identify the most influential factors affecting slope stability and evaluate the performance of various machine learning models for classifying slope stability. Through correlation analysis and feature importance evaluation using a random forest regressor, cohesion, unit weight, slope height, and friction angle were identified as the most critical parameters influencing slope stability. This research assessed the effectiveness of machine learning techniques combined with modern feature selection algorithms and conventional feature analysis methods. The performance of deep learning models, including recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and generative adversarial networks (GANs), in slope stability classification was evaluated. The GAN model demonstrated superior performance, achieving the highest overall accuracy of 0.913 and the highest area under the ROC curve (AUC) of 0.9285. Integration of the binary bGGO technique for feature selection with the GAN model led to significant improvements in classification performance, with the bGGO-GAN model showing enhanced sensitivity, positive predictive value, negative predictive value, and F1 score compared to the classical GAN model. The bGGO-GAN model achieved 95% accuracy on a substantial dataset of 627 samples, demonstrating competitive performance against other models in the literature while offering strong generalizability. This study highlights the potential of advanced machine learning techniques and feature selection methods for improving slope stability classification and provides valuable insights for geotechnical engineering applications. [ABSTRACT FROM AUTHOR]

Most high-mountain regions worldwide are susceptible to snow avalanches during the winter or all year round. In this study, a Universal Snow Avalanche Modeling Index is developed, suitable for determining avalanche hazard in mountain regions. The first step in the research is the collection of data in the field and their processing in geographic information systems and remote sensing. In the period 2023–2024, avalanches were mapped in the field, and later, avalanches as points in geographic information systems (GIS) were overlapped with the dominant natural conditions in the study area. The second step involves determining the main criteria (snow cover, terrain slope, and land use) and evaluating the values to obtain the Snow Avalanche Formation Index (SAFI). Thresholds obtained through field research and the formation of avalanche inventory were used to develop the SAFI index. The index is applied with the aim of identifying locations susceptible to avalanche formation (source areas). The values used for the calculation include Normalized Difference Snow Index (NDSI > 0.6), terrain slope (20–60°) and land use (pastures, meadows). The third step presents the analysis of SAFI locations with meteorological conditions (winter precipitation and winter air temperature). The fourth step is the modeling of the propagation (simulation) of other parts of the snow avalanche in the Flow-R software 2.0. The results show that 282.9 km2 of the study area (Šar Mountains, Serbia) is susceptible to snow avalanches, with the thickness of the potentially triggered layer being 50 cm. With a 5 m thick snowpack, 299.9 km2 would be susceptible. The validation using the ROC-AUC method confirms a very high predictive power (0.94). The SAFI–Flow-R approach offers snow avalanche modeling for which no avalanche inventory is available, representing an advance for all mountain areas where historical data do not exist. The results of the study can be used for land use planning, zoning vulnerable areas, and adopting adequate environmental protection measures. [ABSTRACT FROM AUTHOR]

Urban communities have long been pivotal in wealth creation and technological innovation. In the contemporary context, their modus operandi is intricately tied to a diverse array of critical infrastructure systems (CISs). These systems—encompassing utilities, transportation, communication, and more—are indispensable for daily life; however, historical lessons underscore that the ever-growing interdependence among modern CISs has sapped their robustness. Furthermore, this vulnerability is compounded by the intensifying natural hazards catalysed by climate change, leaving urban communities with eroded resilience. Against this backdrop, pilot studies have harnessed breakthroughs in artificial intelligence (AI) to chart a new course toward resilient urban communities. This paper illuminates AI-driven resilience by reviewing the latest research in key aspects including (1) the limitation of state-of-the-art resilience assessment frameworks; (2) emergency response as a novel blueprint featuring swift response following catastrophes; (3) efficient loss assessment of CISs using AI algorithms; and (4) machine-learning-enabled autonomous emergency response planning. The remaining challenges and hardships faced on the journey toward resilient urban communities are also discussed. The findings could contribute to the ongoing discourse on enhancing urban resilience in the face of increasingly frequent and destructive climate hazards. [ABSTRACT FROM AUTHOR]

Tropical cyclone causes large-scale devastation and destruction in the coastal plains of India, particularly in Odisha, which is the most cyclone-affected state in the country. Tropical cyclones are projected to be more powerful and widespread due to changing climate. Hence, the risk assessment of tropical cyclone is necessary to identify cyclone-risk areas in coastal Odisha which may help in the mitigation of the damages caused by cyclones. Therefore, this study utilizes geospatial techniques to produce a comprehensive risk map posed by tropical cyclones and to estimate the degree of risk for coastal districts of Odisha. For this, we evaluated the district-level cyclone risk for coastal Odisha using multi-criteria decision-making (MCDM) technique by considering 21 parameters for each of the four components of risk, i.e., exposure, hazard, vulnerability, as well as mitigation capacity. For each criterion, thematic raster map layers were created and weighted using a fuzzy analytical hierarchy process (FAHP). We prepared individual risk component maps using weighted overlay techniques and finally integrated all indices to create the risk map. The study shows that 13% area of the study area comes under a very high-risk zone whereas, 25% area comes under a high-risk zone. The central (Cuttack, northern parts of Khordha, and south-western parts of Jajpur district) and the eastern part (most of the parts of Jagatsinghpur, Kendrapara, and northern parts of Puri district) of the study area come under high to very high tropical cyclone impact zone. Almost 67% of the total area is highly vulnerable to tropical cyclones and mainly concentrated near the shoreline. The applied approach and results can assist the local authorities in identifying vulnerable and hazardous locations and developing workable solutions for the mitigation of revised cyclone threats in the coastal districts of Odisha. [ABSTRACT FROM AUTHOR]

Abstract The evaluation of slope stability is of crucial importance in geotechnical engineering and has significant implications for infrastructure safety, natural hazard mitigation, and environmental protection. This study aimed to identify the most influential factors affecting slope stability and evaluate the performance of various machine learning models for classifying slope stability. Through correlation analysis and feature importance evaluation using a random forest regressor, cohesion, unit weight, slope height, and friction angle were identified as the most critical parameters influencing slope stability. This research assessed the effectiveness of machine learning techniques combined with modern feature selection algorithms and conventional feature analysis methods. The performance of deep learning models, including recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and generative adversarial networks (GANs), in slope stability classification was evaluated. The GAN model demonstrated superior performance, achieving the highest overall accuracy of 0.913 and the highest area under the ROC curve (AUC) of 0.9285. Integration of the binary bGGO technique for feature selection with the GAN model led to significant improvements in classification performance, with the bGGO-GAN model showing enhanced sensitivity, positive predictive value, negative predictive value, and F1 score compared to the classical GAN model. The bGGO-GAN model achieved 95% accuracy on a substantial dataset of 627 samples, demonstrating competitive performance against other models in the literature while offering strong generalizability. This study highlights the potential of advanced machine learning techniques and feature selection methods for improving slope stability classification and provides valuable insights for geotechnical engineering applications.

Soil erosion is considered one of the most prevalent natural hazards in semiarid regions, leading to the instability of ecosystems and human life. The main purpose of this research was to investigate and analyze soil erosion susceptibility maps in the Medjerda basin in northern Africa. This study utilizes four ensemble models based on the analytical hierarchy process (AHP) multicriteria decision-making analysis, namely, deep learning neural network AHP (DLNN-AHP), frequency ratio AHP (FR-AHP), Monte Carlo AHP (MC-AHP), and fuzzy AHP (F-AHP). Eight predictor variables were considered as inputs to the model, namely, the slope degree, digital elevation model (DEM), topographic wetness index (TWI), distance to river (DFR), distance to road (DFRD), normalized difference vegetation index (NDVI), rainfall erosivity (R), factor and soil erodibility factor (K). Soil erosion inventory maps were developed from field surveys and satellite images. The dataset was randomly divided into 70% for training and 30% for testing. The performances of the utilized models were compared using a receiver operating characteristic (ROC) curve. The results highlighted that all the models utilized exhibited good performance, with DLNN-AHP (93.1%) exhibiting slight superiority, followed by FR-AHP (90.9%), F-AHP (88.9%), and MC-AHP (88.5%). Among the influencing factors, the distance to the river and rainfall erosivity had the most significant impacts on the incidence of soil erosion. Moreover, the current findings revealed that 38.3% of the study area is extremely highly susceptible to soil erosion. The results of this study can aid in developing decision-support tools for planners and managers aiming to mitigate the adverse effects of soil erosion. [Display omitted] • Enhanced soil erosion assessment using a hybrid MCDM model with improved accuracy. • Deep Learning with AHP boosts soil erosion prediction. • Rainfall erosivity, river proximity are key factors in erosion. • Findings provide tools for effective erosion mitigation. [ABSTRACT FROM AUTHOR]

Abstract The occurrence of landslides is a costly and cataclysmic natural hazard that mainly occurs in hilly areas due to factors like earthquakes, cloud burst, extreme rainfall, human pressure, etc. leading to loss of biodiversity, property, and life. Effective and comprehensive landslide risk management is crucial to address landslide susceptibility. Utilizing remote sensing and Geographic Information System (GIS) techniques, this work focuses on the landslide hazard zonation (LHZ) mapping. The study is specifically conducted in the Doon Valley. These advanced technologies help in identifying and categorizing the areas prone to landslides. The Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM) data at 30 m resolution and Sentinel-2B data at 10 m resolution were used to perform the remote sensing and GIS operations in ArcGIS Software. Thematic layers such as Land Use/Land Cover (LULC), Slope, Geology, Soil Type, Elevation, Drainage Density, Vegetation, and Aspect were produced utilizing remote sensing and GIS data. The weighted overlay, a multi-criteria analysis method, was applied to assign attribute values to each thematic layer based on their importance, which was then combined to calculate the landslide hazard zone. The findings of this study demonstrate that landslides are more likely to occur near and above the Main Boundary Thrust/Fault (MBT). The high to very high hazard zone covers 16.64% of the total area, making landslides occurring more frequently. Additionally, the study found that the Doon Valley rivers’ upper segments are more susceptible to landslides than their lower segments. By integrating Remote Sensing and GIS techniques, it is possible to obtain extensive knowledge of regions prone to landslides. This information will be helpful for decision-makers and planners to reduce the impact of landslides in the near future.

On 5.9.2023, flash floods occurred in Igneada town of Demirkoy district of Kırklareli province, which caused serious loss of life and property. Since this unfortunate event was a natural hazard with a hydro-meteorological characteristic, it occurred at the river basin scale. Therefore, the present study aims to systematically reveal the chain of events leading to the flash floods in the Degirmen River basin, where the natural hazard occurred. The study data were processed and interpreted in the light of the relevant literature within the framework of the findings obtained from office and field studies. At this stage, the Root Cause Analysis (RCA) method was used to determine how and why the event occurred and how to prevent similar problems from occurring again. As a result of the study, it was determined that flash floods caused by sudden torrential rains led to high-energy water flows on the slope lands depending on topographic features. Consequently, they forced the transportation of materials of various sizes. The materials coming from the river's upper basin with strong flash floods with irregularly changing flow rates triggered flooding in places where the slope of the river bed decreased. However, this natural event has become a natural hazard in the lower basin due to the wrong land use caused by the negative anthropogenic effects of human beings on the natural environment.

This document is an editorial from the journal Frontiers in Earth Science titled "Prevention, Mitigation, and Relief of Compound and Chained Natural Hazards." It discusses the increasing frequency of extreme natural disasters due to global climate warming and frequent earthquakes, which pose significant threats to human life and property. The editorial highlights the importance of preventing, mitigating, and relieving compound and chained natural hazards, and the role of technological advancements in addressing these hazards. The document provides an overview of nine published papers that focus on earthquakes, geological hazards, earthquake-triggered landslides, and landslide susceptibility. It concludes by emphasizing the need for continued research on comprehensive natural hazards and disaster chains, beyond earthquakes and geological disasters, such as meteorological events, floods, droughts, wildfires, and tsunamis. [Extracted from the article]

The mainstream approach of social vulnerability to flood risk faces some challenges regarding its ability to address the complexity of its causal processes. The objective of this study was to analyse the causal processes of social vulnerability to flood impacts from a relational perspective. To this end, a social network analysis was performed that identified the conditions and causes of social vulnerability and systematically articulated the relationships between them. This analysis was tested on the specific case of flood risk on the coast of the province of Alicante (SE Spain). To ascertain the conditions and causes of social vulnerability to flood risk, a multidisciplinary group of local experts was consulted, and the resulting data processed in a relational way using Atlas.ti and Gephi softwares. The result was a social vulnerability network comprising 84 nodes and 189 edges distributed into four dimensions: the adaptive capacity of tourists, socio-economic structure, land use planning and risk management. The information was ranked for betweenness centrality, revealing the components with highest causal power of social vulnerability to social impacts in flooding events: low flood risk awareness, economic growth based on real estate boom, property speculation and lack of political interest in flood risk management. This proposal places emphasis on the driving forces of social vulnerability and not exclusively on the specific adaptive conditions of the population, which allows a strategic identification and management of generative forces that ultimately induce the social impacts of floods. [ABSTRACT FROM AUTHOR]

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On 5.9.2023, flash floods occurred in Igneada town of Demirkoy district of Kırklareli province, which caused serious loss of life and property. Since this unfortunate event was a natural hazard with a hydro-meteorological characteristic, it occurred at the river basin scale. Therefore, the present study aims to systematically reveal the chain of events leading to the flash floods in the Degirmen River basin, where the natural hazard occurred. The study data were processed and interpreted in the light of the relevant literature within the framework of the findings obtained from office and field studies. At this stage, the Root Cause Analysis (RCA) method was used to determine how and why the event occurred and how to prevent similar problems from occurring again. As a result of the study, it was determined that flash floods caused by sudden torrential rains led to high-energy water flows on the slope lands depending on topographic features. Consequently, they forced the transportation of materials of various sizes. The materials coming from the river's upper basin with strong flash floods with irregularly changing flow rates triggered flooding in places where the slope of the river bed decreased. However, this natural event has become a natural hazard in the lower basin due to the wrong land use caused by the negative anthropogenic effects of human beings on the natural environment. [ABSTRACT FROM AUTHOR]

Lightning fatalities in the Philippines are analyzed for the first time using data from the Philippine Statistics Authority. A total of 1584 lightning fatalities occurred between 2006 and 2021. The annual number of lightning fatalities decreased from an average of 118 fatalities per year between 2006 and 2013 to an average of 80 fatalities per year between 2014 and 2021. The majority of the fatalities occurred in May to July (56%), during the afternoon, and in provincial areas (95%). About 82% of the victims were male where the most vulnerable group are young males ages 20 to 29 years old. Common activities of the victims include sheltering in non-enclosed infrastructures such as agricultural huts, and doing outdoor laborious work such as farming and fishing. Immediate strategic interventions can focus on raising lightning safety awareness in high-risk areas and among the vulnerable population. [ABSTRACT FROM AUTHOR]

Early warning systems are an essential tool for managing flood emergencies. Alert and warning applications and mobile-phone messaging services have become increasingly widespread among major international emergency agencies as means of communicating risks to the population, and their effectiveness in reducing human and material damages during flood events is significant. Despite their crucial importance, one of the main challenges in the field of emergency communication is the lack of protocols for systematic and standardized production of warning messages. While emergency agencies produce messages on a diversity of topic areas, there are no protocols for structuring their content according to communication functions, exhaustive identification of the relevant areas of action, or classification of content according to different topics. With a view to this opportunity for improvement, the aim of this article is to propose a method for creating a catalog of warning messages enabling their systematic composition and organization. To exemplify the successive stages in the development of such a catalog, we present here the resources and methodological process followed by the authors of this article when commissioned with this task by the emergency services of the Valencian Autonomous Region (south-east Spain) for flood-risk communication. The warning message catalog was pilot tested with experts and user focus groups. Developing warning message catalogs offers a vital resource that can enhance the outreach and operability of warning systems in the current context of increased flood risk due to climate change. [ABSTRACT FROM AUTHOR]

As extreme weather events increase in frequency and intensity, the health system faces significant challenges, not only from shifting patterns of climate-sensitive diseases but also from disruptions to healthcare infrastructure, supply chains and the physical systems essential for delivering care. This necessitates the strategic use of geospatial tools to guide the delivery of healthcare services and make evidence-informed priorities, especially in contexts with scarce human and financial resources. In this article, we highlight several published papers that have been used throughout the phases of the disaster management cycle in relation to health service delivery. We complement the findings from these publications with a rapid scoping review to present the body of knowledge for using spatial methods for health service delivery in the context of disasters. The main aim of this article is to demonstrate the benefits and discuss the challenges associated with the use of geospatial methods throughout the disaster management cycle. Our scoping review identified 48 articles employing geospatial techniques in the disaster management cycle. Most of them focused on geospatial tools employed for preparedness, anticipatory action and mitigation, particularly for targeted health service delivery. We note that while geospatial data analytics are effectively deployed throughout the different phases of disaster management, important challenges remain, such as ensuring timely availability of geospatial data during disasters, developing standardized and structured data formats, securing pre-disaster data for disaster preparedness, addressing gaps in health incidence data, reducing underreporting of cases and overcoming limitations in spatial and temporal coverage and granularity. Overall, existing and novel geospatial methods can bridge specific evidence gaps in all phases of the disaster management cycle. Improvement and ‘operationalization’ of these methods can provide opportunities for more evidence-informed decision making in responding to health crises during climate change.

Situated within the Ring of Fire and characterized by a tropical climate and high seismic activity, Indonesia is uniquely vulnerable to natural disasters such as floods and landslides. These events pose significant threats to both the population and infrastructure. This study predicts areas exposed to flood and landslide risk by considering various environmental factors related to climate, topography, and land use. The predictive performance of three machine learning models—naïve Bayes, k-nearest neighbors, and random forest (RF)—was evaluated by comparing the AUC, RMSE, and R2 values of each model. Ultimately, the RF model, which demonstrated the highest accuracy, was used to prioritize disaster impact factors and generate hazard maps. The results identified the interaction of rainfall, land use, and slope aspect as the most critical determinants of hazard occurrence. The predicted hazard maps revealed that approximately 26.7 % of the study area was vulnerable to either floods or landslides, with 16.8 % of the area experiencing both. The new capital of Nusantara showed a relatively higher multi-hazard risk than did the overall study area and protected zones, with 22.1 % of the hazard area vulnerable to both flooding and landslides. In single hazard zones, areas classified as at risk for floods had a higher mean probability of experiencing both hazards (43 %), as compared to areas classified as at risk for landslides (22 %). As a result, urban planners and relevant stakeholders can now utilize the hazard maps developed in this study to prioritize infrastructure reinforcement and disaster risk areas, integrating land use planning with risk assessment to mitigate the impact of disasters. By employing these strategies, Indonesia and other countries facing similar challenges can now enhance their disaster preparedness and response capabilities in new capital regions and other areas, ultimately planning for more sustainable urban development.

The U.S. National Institute of Standards and Technology (NIST) published the Community Resilience Planning Guide in 2016. The NIST Guide advocates for a participatory process for developing a performance measurement framework for the jurisdiction's resilience against a scenario hazard. The framework centers around tables of expected and desired recovery times for selected community assets, such as electricity, water, and natural gas infrastructures. The NIST Guide does not provide a method for estimating the expected recovery times. However, building high-fidelity computer models for such estimations requires substantial resources that even larger jurisdictions cannot cost-justify. The most promising approach to recovery time estimation is to systematically use data elicited from people to tap into the wisdom of the (knowledgeable) crowd. This paper describes a novel research-through-design project to enable the computer-supported elicitation of recovery time series data. This work is the first in the literature to examine people's ability to estimate recovery curves and how design influences such estimations. Its main contribution to resilience planning is three-fold: development of a new elicitation tool called Restimate, understanding its potential user base, and providing insights into how it can facilitate resilience planning. Restimate is the first tool to enable evidence-based expert elicitation in any community with limited resources for resilience planning. Beyond resilience planning, those who facilitate high-stakes planning activities under large uncertainties (e.g., mission-critical system design and planning) will benefit from a similar research-through-design process.

In response to the urgent global threat posed by human-induced extreme climate hazards, heatwaves are still systematically under-reported and under-researched in Thailand. This region is confronting a significant rise in heat-related mortality, which has resulted in hundreds of deaths, underscoring a pressing issue that needs to be addressed. This research article is one of the first to present a solution for assessing heatwave dynamics, using machine learning (ML) algorithms and geospatial technologies in this country. It analyzes heatwave metrics like heatwave number (HWN), heatwave frequency (HWF), heatwave duration (HWD), heatwave magnitude (HWM), and heatwave amplitude (HWA), combining satellite-derived land surface temperature (LST) data with ground-based air temperature ( T air ) observations from 1981 to 2019. The result reveals significant marked increases in both the frequency and intensity of daytime heatwaves in peri-urban areas, with the most pronounced changes being a 0.45-day/year in HWN, a 2.00-day/year in HWF, and a 0.27-day/year in HWD. This trend is notably less pronounced in urban areas. Conversely, rural regions are experiencing a significant escalation in nighttime heatwaves, with increases of 0.39 days/year in HWN, 1.44 days/year in HWF, and 0.14 days/year in HWD. Correlation analysis ( p < 0.05 ) reveals spatial heterogeneity in heatwave dynamics, with robust daytime correlations between T air and LST in rural (HWN, HWF, HWD, r > 0.90 ) and peri-urban (HWM, HWA, r > 0.65 ) regions. This study emphasizes the importance of considering microclimatic variations in heatwave analysis, offering insights for targeted intervention strategies. It demonstrates how enhancing remote sensing with ML can facilitate the spatial–temporal analysis of heatwaves across diverse environments. This approach identifies critical risk areas in Thailand, guiding resilience efforts and serving as a model for managing similar microclimates, extending the applicability of this study. Overall, the study provides policymakers and stakeholders with potent tools for climate action and effective heatwave management. Furthermore, this research contributes to mitigating the impacts of extreme climate events, promoting resilience, and fostering environmental sustainability. [ABSTRACT FROM AUTHOR]

Accurate assessment of surface water from satellite and remote sensing data plays an important role in water and flood management and supporting natural ecosystems and human development. Remote sensing imagery has significantly advanced in water extraction methods, particularly in water index, classification, and sub-pixel analysis. Water-index-based approaches offer notable advantages such as speed and convenience among these methods. The unique characteristics of surface water and flooded areas, including their extensive coverage and dynamic nature, make the water index particularly effective for monitoring large regions. However, the complexity of land surfaces in aquatic environments presents challenges that hinder accurate water extraction. These challenges differ across various factors, such as shadows in urban and mountainous areas, small water bodies, muddy water, and water leakage in unshaded regions. The current study introduces a novel Flood/Water Extraction Index (FWEI) for identifying water and flooded areas to address these challenges. The FWEI utilizes the average ratio of visible and near-infrared bands derived from Sentinel-2 images. The proposed index utilizes images with 10-m and average visible bands and more effectively compensates for errors arising from spectral and spatial changes. Therefore, it demonstrates strong performance by more accurately mapping muddy and clear water within small water bodies and narrow rivers. The performance of the offered FWEI index is compared with other indices, including the Normalized Difference Water Indices (NDWI-G, NDWI-F), Modified NDWI (MNDWI-1, MNDWI-2), and the Automatic Water Extraction Index (AWEInsh) without shadow. While other indices excel in specific scenarios, such as built-up or non-built-up areas, and bare lands versus vegetated areas, the FWEI index demonstrates consistently high accuracy and stability in extracting surface water across diverse backgrounds. The FWEI index achieves an average Overall Accuracy (OA) of 94.26% for water extraction and 93.11% for flood extraction. In comparison, the AWEInsh attains an OA of 90.48% and 90.39%, NDWI-F performs at 86.69% and 86.55%, MNDWI-1 at 77.21% and 75.82%, MNDWI-2 at 76.12% and 75.42%, and NDWI-G at 75.26% and 74.78%, respectively. The integration of visible spectral bands with the near-infrared band proves instrumental in enhancing the accuracy of water derivation in complex and expansive environments. [ABSTRACT FROM AUTHOR]

Changes in land cover (LC) are continuously growing due to natural and human factors, and they are even occurring within protected areas. In Europe, one such place is the Tatra region (southern Poland), which is the area of the presented research. Dynamic landscape transformation in a valuable natural area can negatively affect both environmental quality and sustainable land use planning. Appropriate recommendations and treatments can be introduced to prevent negative effects. To do this, it is necessary to understand the dynamics and character of these transformations. Therefore, the purpose of the article is to analyse changes in LC, its causes and their spatiotemporal dynamics, as well as the resulting landscape fragmentation. For this purpose, a set of landscape metrics and GIS (Geographic Information System) tools were used. The main data source in the study was the CORINE Land Cover (CLC) database. The results indicate a significant landscape transformation with a negative impact on the region's environment, even in protected areas. Landscape fragmentation, which causes disturbances in ecosystems, increased across the entire study area. In turn, in the spatial development, it can introduce chaos and spatial disorder, and lead to the phenomenon of urban sprawl. [ABSTRACT FROM AUTHOR]

The U.S. National Institute of Standards and Technology (NIST) published the Community Resilience Planning Guide in 2016. The NIST Guide advocates for a participatory process for developing a performance measurement framework for the jurisdiction's resilience against a scenario hazard. The framework centers around tables of expected and desired recovery times for selected community assets, such as electricity, water, and natural gas infrastructures. The NIST Guide does not provide a method for estimating the expected recovery times. However, building high-fidelity computer models for such estimations requires substantial resources that even larger jurisdictions cannot cost-justify. The most promising approach to recovery time estimation is to systematically use data elicited from people to tap into the wisdom of the (knowledgeable) crowd. This paper describes a novel research-through-design project to enable the computer-supported elicitation of recovery time series data. This work is the first in the literature to examine people's ability to estimate recovery curves and how design influences such estimations. Its main contribution to resilience planning is three-fold: development of a new elicitation tool called Restimate, understanding its potential user base, and providing insights into how it can facilitate resilience planning. Restimate is the first tool to enable evidence-based expert elicitation in any community with limited resources for resilience planning. Beyond resilience planning, those who facilitate highstakes planning activities under large uncertainties (e.g., mission-critical system design and planning) will benefit from a similar research-through-design process. [ABSTRACT FROM AUTHOR]

Floods are a destructive disaster in Iran. The case of Kan River Basin is a representative example of the dynamics of seasonal and extraordinary rains in floodplains associated with the Niña phenomenon, storms, low pressure systems and tropical cyclones. The tectonic, geological, geomorphological, biological and climatological characteristics draw this relief with intense dynamics. They along with the population growth without effective planning of the territory generate ideal risk scenarios for the disaster occurrence with greater intensities. A morphometric and morphogenetic analysis is carried out, which gives a flood hazard map of the basin with four susceptibility types: the apparent seasonal beds, the maximum floodplain, the extraordinary floodplain, and the areas of low susceptibility to occurrence. This map is validated through the DesInventar disasters database with a 95.08% of confirmation. This cartography and its respective analysis are a basic input for the risk management and the territorial ordering of this river basin. [ABSTRACT FROM AUTHOR]

This study explores how the System Dynamics modeling approach can help deal with the problem of conventional insurance mechanisms by studying the feedback loops governing complex systems connected to the disaster insurance mechanism. Instead of addressing the disaster's underlying risk, the traditional disaster insurance strategy largely focuses on providing financial security for asset recovery after a disaster. This constraint becomes especially concerning as the threat of climate-related disasters grows since it may result in rising long-term damage expenditures. A new insurance mechanism is suggested as a solution to this problem to lower damage costs while safeguarding insured assets and luring new assets to be protected. A local case study utilizing a System Dynamics stock and flow model is created and validated by examining the model's structure, sensitivity analysis, and extreme value test. The results of the case study performed on a city in Latvia highlight the significance of effective disaster risk reduction strategies applied within the innovative insurance mechanism in lowering overall disaster costs. The logical coherence seen throughout the analysis of simulated scenario results strengthens the established model's plausibility. The case study's findings support the innovative insurance mechanism's dynamic hypothesis and show the main influencing factors on the dynamics within the proposed innovative insurance mechanism. The information this study can help insurance firms, policy planners, and disaster risk managers make decisions that will benefit local communities and other stakeholders regarding climate-related disaster risk mitigation. [ABSTRACT FROM AUTHOR]

Typhoons are destructive multi-hazard events. To assess the relationship between typhoon intensity and agricultural loss, there is a need for accurate and standardized information on loss and damage, which is currently lacking. To address this, a database for Vietnam and the Philippines was created to provide aggregated information on the magnitude of rice damage and to highlight the rice-growing areas which were prone to being damaged by typhoons. Our study period was from 1970 to 2018, and we focused on Vietnam and the Philippines as these two countries experience frequent and intense typhoons. As different crops respond differently to wind and rain, we limit our research to a single crop. In this study, we focused on rice as it is a major staple food in Southeast Asia, and rice fields were often damaged by typhoons in the two countries. Of the 829 typhoon events recorded, only 15% of the events resulted in rice damage. The average area of rice damaged per typhoon event ranged from 42,407 ha in Vietnam to 83,571 ha in the Philippines. Meanwhile, the average production loss per typhoon event ranged from 190,227 metric tonnes in the Philippines to 539,150 metric tonnes in Vietnam. The monetary value of rice crops lost was only reported in the Philippines, and this amounted to an average of US$ 42 million per typhoon event. There was a weak relationship between landfall wind speed and the three indicators of rice damage, which suggests that rice damage was not primarily due to strong winds. Our results showed that the rice fields in the coastal provinces of Vietnam and the northern parts of the Philippines were more vulnerable to being damaged by typhoons. [ABSTRACT FROM AUTHOR]

Landslides are a prevalent natural hazard in West Bengal, India, particularly in Darjeeling and Kurseong, resulting in substantial socio-economic and physical consequences. This study aims to develop a hybrid model, integrating a Genetic-based Random Forest (GA-RF) and a novel Self-Attention based Convolutional Neural Network and Long Short-term Memory (SA-CNN-LSTM), for accurate landslide susceptibility mapping (LSM) and generate landslide vulnerability-building map in these regions. To achieve this, we compiled a database with 1830 historical data points, incorporating a landslide inventory as the dependent variable and 32 geo-environmental parameters from Remote Sensing (RS) and Geographic Information Systems (GIS) layers as independent variables. These parameters include features like topography, climate, hydrology, soil properties, terrain distribution, radar features, and anthropogenic influences. Our hybrid model exhibited superior performance with an AUC of 0.92 and RMSE of 0.28, outperforming standalone SA-CNN-LSTM, GA-RF, RF, MLP, and TreeBagger models. Notably, slope, Global Human Modification (gHM), Combined Polarization Index (CPI), distances to streams and roads, and soil erosion emerged as key layers for LSM in the region. Our findings identified around 30% of the study area as having high to very high landslide susceptibility, 20% as moderate, and 50% as low to very low. The vulnerability-building map for 244,552 building footprints indicated varying landslide risk levels, with a significant proportion (27.74%) at high to very high risk. Our model highlighted high-risk zones along roads in the northeastern and southern areas. These insights can enhance landslide risk management in Darjeeling and Kurseong, guiding sustainable strategies for future damage qualification.

The Remote Sensing (RS) field continuously grapples with the challenge of transforming satellite data into actionable information. This ongoing issue results in an ever-growing accumulation of unlabeled data, complicating interpretation efforts. The situation becomes even more challenging when satellite data must be used immediately to identify the effects of a natural hazard. Self-supervised learning (SSL) offers a promising approach for learning image representations without labeled data. Once trained, an SSL model can address various tasks with significantly reduced requirements for labeled data. Despite advancements in SSL models, particularly those using contrastive learning methods like MoCo, SimCLR, and SwAV, their potential remains largely unexplored in the context of instance segmentation and semantic segmentation of satellite imagery. This study integrates SwAV within an auto-encoder framework to detect landslides using deca-metric resolution multi-spectral images from the globally-distributed large-scale landslide4sense (L4S) 2022 benchmark dataset, employing only 1% and 10% of the labeled data. Our proposed SSL auto-encoder model features two modules: SwAV, which assigns features to prototype vectors to generate encoder codes, and ResNets, serving as the decoder for the downstream task. With just 1% of labeled data, our SSL model performs comparably to ten state-of-the-art deep learning segmentation models that utilize 100% of the labeled data in a fully supervised manner. With 10% of labeled data, our SSL model outperforms all ten fully supervised counterparts trained with 100% of the labeled data.

Typhoons are destructive multi-hazard events. To assess the relationship between typhoon intensity and agricultural loss, there is a need for accurate and standardized information on loss and damage, which is currently lacking. To address this, a database for Vietnam and the Philippines was created to provide aggregated information on the magnitude of rice damage and to highlight the rice-growing areas which were prone to being damaged by typhoons. Our study period was from 1970 to 2018, and we focused on Vietnam and the Philippines as these two countries experience frequent and intense typhoons. As different crops respond differently to wind and rain, we limit our research to a single crop. In this study, we focused on rice as it is a major staple food in Southeast Asia, and rice fields were often damaged by typhoons in the two countries. Of the 829 typhoon events recorded, only 15% of the events resulted in rice damage. The average area of rice damaged per typhoon event ranged from 42,407 ha in Vietnam to 83,571 ha in the Philippines. Meanwhile, the average production loss per typhoon event ranged from 190,227 metric tonnes in the Philippines to 539,150 metric tonnes in Vietnam. The monetary value of rice crops lost was only reported in the Philippines, and this amounted to an average of US$ 42 million per typhoon event. There was a weak relationship between landfall wind speed and the three indicators of rice damage, which suggests that rice damage was not primarily due to strong winds. Our results showed that the rice fields in the coastal provinces of Vietnam and the northern parts of the Philippines were more vulnerable to being damaged by typhoons.

Lacustrine sediments store a wealth of information and are often used to indicate climate change. Natural disasters such as earthquake, flood, landslide or collapse usually form characteristic disturbances in lacustrine sediments, which can be analyzed and identified using lake geology methods. This paper reviews the research progress of lacustrine sediments, systematically summarizes the characteristics of typical natural disasters in lacustrine sediments, and focuses on the identification methods of natural disaster events in lacustrine sediments. Among them, the comprehensive analysis method integrates various analysis methods for sedimentary identification of lake events at the present stage. ① Identify the endogenous and exogenous origin based on the relationship between sedimentary facies and event-induced deposits(EID)；② Recognize the most likely trigger based on the elimination of impossible triggers；③ Verify the correctness of the most likely trigger according to the existing identification criteria. This method integrates the advantages of typical mark analysis and exclusion method, and is the most effective analysis method at present. This paper also discusses the advantages, disadvantages and constraints of the study of lacustrine sediment disaster events. The continuity, sensitivity and high resolution of lacustrine sediments are conducive to the study of disaster events, but the establishment of sedimentary identification markers of typical natural disaster events in lakes is still the key to restricting the study of lake abnormal events in the future.

Most high-mountain regions worldwide are susceptible to snow avalanches during the winter or all year round. In this study, a Universal Snow Avalanche Modeling Index is developed, suitable for determining avalanche hazard in mountain regions. The first step in the research is the collection of data in the field and their processing in geographic information systems and remote sensing. In the period 2023–2024, avalanches were mapped in the field, and later, avalanches as points in geographic information systems (GIS) were overlapped with the dominant natural conditions in the study area. The second step involves determining the main criteria (snow cover, terrain slope, and land use) and evaluating the values to obtain the Snow Avalanche Formation Index (SAFI). Thresholds obtained through field research and the formation of avalanche inventory were used to develop the SAFI index. The index is applied with the aim of identifying locations susceptible to avalanche formation (source areas). The values used for the calculation include Normalized Difference Snow Index (NDSI > 0.6), terrain slope (20–60°) and land use (pastures, meadows). The third step presents the analysis of SAFI locations with meteorological conditions (winter precipitation and winter air temperature). The fourth step is the modeling of the propagation (simulation) of other parts of the snow avalanche in the Flow-R software 2.0. The results show that 282.9 km2 of the study area (Šar Mountains, Serbia) is susceptible to snow avalanches, with the thickness of the potentially triggered layer being 50 cm. With a 5 m thick snowpack, 299.9 km2 would be susceptible. The validation using the ROC-AUC method confirms a very high predictive power (0.94). The SAFI–Flow-R approach offers snow avalanche modeling for which no avalanche inventory is available, representing an advance for all mountain areas where historical data do not exist. The results of the study can be used for land use planning, zoning vulnerable areas, and adopting adequate environmental protection measures.

Urban communities have long been pivotal in wealth creation and technological innovation. In the contemporary context, their modus operandi is intricately tied to a diverse array of critical infrastructure systems (CISs). These systems—encompassing utilities, transportation, communication, and more—are indispensable for daily life; however, historical lessons underscore that the ever-growing interdependence among modern CISs has sapped their robustness. Furthermore, this vulnerability is compounded by the intensifying natural hazards catalysed by climate change, leaving urban communities with eroded resilience. Against this backdrop, pilot studies have harnessed breakthroughs in artificial intelligence (AI) to chart a new course toward resilient urban communities. This paper illuminates AI-driven resilience by reviewing the latest research in key aspects including (1) the limitation of state-of-the-art resilience assessment frameworks; (2) emergency response as a novel blueprint featuring swift response following catastrophes; (3) efficient loss assessment of CISs using AI algorithms; and (4) machine-learning-enabled autonomous emergency response planning. The remaining challenges and hardships faced on the journey toward resilient urban communities are also discussed. The findings could contribute to the ongoing discourse on enhancing urban resilience in the face of increasingly frequent and destructive climate hazards.

In India, south-west monsoon winds become weak and monsoon withdraws continuously during October. Rainfall during October is a common phenomenon because October is the transition month from the rainy season to the dry winter conditions. However, occasional rainfall activity continues during October. By the beginning of October 2021, the monsoon withdrew from the northern plains. Uttarakhand experienced exceptionally heavy rainfall, which caused floods and severe damage to the economy and human life in the state from 16 to 20 October 2021. A deep westerly trough lay between longitude 65.0° E to 70.0° E and a low-pressure area observed over east Rajasthan along with moisture incursion from the Bay of Bengal as well as from the Arabian Sea over the western Himalayan region, particularly over Uttarakhand. Moisture incursion from the Arabian Sea is one of the important factor contributing to heavy rainfall. The case study was conducted using various meteorological observations, wind patterns, and weather systems. The current year's rainfall pattern and normal rainfall trend were analysed using rainfall data for October 2021. It is observed that rainfall was received from 13 stations and all the stations of the state received rain heavier than normal during the third week of October 2021, which indicates that exceptionally heavy rain generally, recurved during the third week of October. Actual vs. normal rainfall is showing R2 0.61 which reveals that 61% of the variability observed in the actual variable and is explained by the regression model. [ABSTRACT FROM AUTHOR]

Volcanic eruptions provide broad spectral forcing to the atmosphere and understanding the primary mechanisms that are relevant to explain the variety in waveform characteristics in the Ionosphere‐Thermosphere (IT) is still an important open question for the community. In this study, Global Navigation Satellite System (GNSS) Total Electron Content (TEC) data are analyzed and compared to simulations performed by the Global Ionosphere‐Thermosphere Model with Local Mesh Refinement (GITM‐R) for the first phase of the 2015 Calbuco eruption that occurred on 22 April. A simplified source representation and spectral acoustic‐gravity wave (AGW) propagation model are used to specify the perturbation at the lower boundary of GITM‐R at 100 km altitude. Two assumptions on the propagation structure, Direct Spherical (DS) and Ground Coupled (GC), are compared to the GNSS data and these modeling specifications show good agreement with different aspects of the observations for some waveform characteristics. Most notably, GITM‐R is able to reproduce the relative wave amplitude of AGWs as a function of radial distance from the vent, showing acoustic dominant forcing in the near field (<500 km) and gravity dominant forcing in the far‐field (>500 km). The estimated apparent phase speeds from GITM‐R simulations are consistent with observations with ∼10% difference from observation for both acoustic wave packets and a trailing gravity mode. The relevance of the simplifications made in the lower atmosphere to the simulated IT response is then discussed. Plain Language Summary: The two eruption phases of the 2015 Calbuco volcanic event created atmospheric pressure and gravity disturbances that were measured as plasma disturbances in the earth's upper atmosphere by global navigation satellites. This study utilizes a fully self‐consistent global circulation model of the upper atmosphere, with a highly flexible resolution, to simulate and investigate our understanding of the coupled atmosphere‐plasma system in the event of a volcanic eruption. It is shown that the current methodology is capable of recreating important features of the observed upper atmospheric signals which include magnitude distributions, arrival times, and the relative contributions of pressure and gravity influenced waves as a function of distance from the volcano. Key Points: GNSS TEC data analysis for the first phase of the 2015 Calbuco eruption shows varied acoustic (A) and gravity (G) dominant perturbationsA simplified source representation and spectral A‐G wave model are used to drive GITM‐R to capture meso‐scale perturbations near the sourceThe relative amplitude and phase speeds of acoustic and gravity wave driven ionospheric disturbance are reproduced in GITM‐R simulations [ABSTRACT FROM AUTHOR]

Copyright of Progress in Earthquake Sciences is the property of China Earthquake Administration, Institute of Geophysics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Accurate prediction of storm surges is crucial for mitigating the impact of extreme weather events. This paper introduces the Bidirectional Attention-based Long Short-Term Memory (LSTM) Storm Surge Architecture, BALSSA, addressing limitations in traditional physical models. By leveraging machine learning techniques and extensive historical and real-time data, BALSSA significantly enhances prediction accuracy. Utilizing a bidirectional attention-based LSTM framework, it captures complex, non-linear relationships and long-term dependencies, improving the accuracy of storm surge predictions. The enhanced model, D-BALSSA, further amplifies predictive capability through a doubled bidirectional attention-based structure. Training and evaluation involve a comprehensive dataset from over 70 typhoon incidents in Macao between 2017 and 2022. The results showcase the outstanding performance of BALSSA, delivering highly accurate storm surge forecasts with a lead time of up to 72 h. Notably, the model exhibits a low Mean Absolute Error (MAE) of 0.0287 m and Root Mean Squared Error (RMSE) of 0.0357 m, crucial indicators measuring the accuracy of storm surge predictions in water level anomalies. These metrics comprehensively evaluate the model's accuracy within the specified timeframe, enabling timely evacuation and early warnings for effective disaster mitigation. An adaptive system, integrating real-time alerts, tropical cyclone (TC) chaser, and prospective visualizations of meteorological and tidal measurements, enhances BALSSA's capabilities for improved storm surge prediction. Positioned as a comprehensive tool for risk management, BALSSA supports decision makers, civil protection agencies, and governments involved in disaster preparedness and response. By leveraging advanced machine learning techniques and extensive data, BALSSA enables precise and timely predictions, empowering coastal communities to proactively prepare and respond to extreme weather events. This enhanced accuracy strengthens the resilience of coastal communities and protects lives and infrastructure from the escalating threats of climate change. [ABSTRACT FROM AUTHOR]

Increasing intensity and frequency of tornadoes have been reported in Europe in the last decades, and in Italy, in particular in the northeast area, including Veneto, Friuli Venezia Giulia and Trentino Alto Adige. The public consciousness and the meteorological control of this risk are sufficiently established in the USA. The European continent and the northeast of Italy do not exhibit a similar state, although the tornado damages induced are increasing and becoming of economic relevance. Studies and research are diffused and have reached a detailed level, being able to correlate tornado intensity to a detailed damage state for a wide amount of building classes. But, codes and standard, both in USA and in the whole European continent, have not been developed for tornado loadings. For this reason, to fill the gap between studies, research and observational data, in this study Italian wind extreme events are presented and discussed first. In a second part, the damage assessment results of the most extreme events recorded in the northeast of Italy (from F3 to F5, Fujita scale) from 1905 to 2017 are presented. Codes and standard showing the lack of design and verification procedure are presented in the third part. Finally, a tornado-resistant building classification is presented to enforce the consciousness that new codes and standard could protect people from extreme events, decreasing dramatically the devastation of not so rare phenomena also for Italy. [ABSTRACT FROM AUTHOR]

Tropical cyclones (TCs) are one of the most destructive natural hazards to impact on Australia's population, infrastructure, and the environment. To examine potential TC impacts, it is important to understand which assets are exposed to the hazard and of these, which are vulnerable to damage. The aim of this study is to improve TC risk assessments through developing an exposure–vulnerability index, utilising a case study for the six Local Government Areas (LGAs) impacted by the landfall of TC Debbie in 2017: Burdekin Shire, Charters Towers Region, Isaac Region, Mackay Region, City of Townsville, and Whitsunday Region. This study utilised a natural hazard risk assessment methodology, linking exposure and vulnerability indicators related to social factors, infrastructure, and the environment. The two LGAs with the most extreme exposure–vulnerability values were the coastal regions of Mackay Region and the City of Townsville. This is consistent with urbanisation and city development trends, with these LGAs having more people (social) and infrastructure exposed, while the environmental domain was more exposed and vulnerable to TC impacts in rural LGAs. Therefore, further resilience protocols and mitigation strategies are required, particularly for Mackay Region and the City of Townsville, to reduce the damage and ultimate loss of lives and livelihoods from TC impacts. This study serves as a framework for developing a TC risk index based on hazard, exposure, and vulnerability indices, and insight into the improved mitigation strategies for communities to implement in order to build resilience to the impacts of future TCs. [ABSTRACT FROM AUTHOR]

Parametric insurance schemes allow for payouts to be triggered by real-time hydro-/meteorological parameters instead of waiting for damage assessments, which means they can be settled swiftly, giving people access to funds right after the event. In this work we propose a framework to design parametric insurance schemes and systematically quantify the basis risk: the difference between the parameter-based payout and the actual damage. We implement the framework in the open-source global risk assessment platform CLIMADA and illustrate it with two stylized parametric insurance case studies, targeting tropical cyclones in Mozambique and winter storms in France. The data used and the provided code base are globally-consistent, open-source, and readily available. The presented methods are therefore applicable in data-scarce areas and accessible to stakeholders from the public and private sector. Moreover, our approach can easily be adapted to other hazards and exposures worldwide. This improves the accessibility and transparency of such innovative insurance schemes. [ABSTRACT FROM AUTHOR]

Copyright of Mugla Journal of Science & Technology is the property of Mugla Journal of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

According to the Icelandic Meteorological Office, climate change may increase the likelihood, intensity, and frequency of some natural hazards in the country, such as avalanches. It is therefore essential to increase preparedness for climate change–related effects. Until recently, social and community aspects of climate change–related hazards have not received much attention in Iceland. The aim of this study was to explore the role of place attachment in small remote communities in Iceland and how residents experience the threat of avalanches. Through a narrative approach, we conducted interviews with residents and focus groups in two communities in the Westfjords. Our findings suggest that both communities show a high level of place attachment, in particular with regard to the natural and social dimension. A positive impact of place attachment translates into willingness for volunteering and local engagement, which can increase preparedness and enhance capacity building. While we found negative tendencies of place attachment in both places, for example in that residents do not consider their communities vulnerable despite the risk, they seem to be less prominent than the positive aspects. Residents exhibit traditional local knowledge, but there is a need to better integrate newcomers and foreigners in the communities into emergency planning. [ABSTRACT FROM AUTHOR]