Search Results (59)

Population growth and unplanned land use significantly contribute to transforming natural hazards into disasters. Earthquake-induced losses of life and property are often linked to inadequate planning decisions. The city center of Şanlıurfa provides a recent example, where the 6 February 2023 earthquake resulted in 340 fatalities and substantial material damage. Variations in urban planning over different periods have caused disaster risk to fluctuate even across short distances. This study examines Şanlıurfa’s urban development in terms of earthquake vulnerability. Using Geographic Information Systems (GIS) and the Analytic Hierarchy Process (AHP), the earthquake risk map reveals elevated risk in areas near fault lines and regions with high groundwater levels. Approximately 7% of the area is classified as very low risk, 54% as low risk, 37% as moderate risk, and 2% as high risk. Limited consideration of disaster-focused planning has led to both planned and unplanned developments in hazardous zones. Consequently, construction should prioritize low-risk areas, with necessary precautions applied in high-risk zones when unavoidable. Full article

On 10 March 1933, an earthquake of roughly 6.4 on the Richter scale (retrospectively estimated) hit the City of Long Beach, California, and the counties surrounding it. Seismically, the quake was of moderate magnitude. However, to this day it remains one of the most destructive quakes in California history in terms of structural damage and fatalities, largely because of faults in building construction of the time that resulted in widespread collapses resulting from earth movement. This article tells the story of the quake itself in full detail; examines its role in the passage of the Field Act, tracing out how that act has impacted earthquake-resistant building design policy, law and practice in California and beyond; assesses the way in which the earthquake altered the trajectory of earthquake science; and details the economic policy response to the quake and the short-term stimulative effects this had on Long Beach and Southern California economies (referred to here as “Disaster Keynesianism”). While there is a large historiographical literature on the Long Beach quake and some of its singular impacts, this research is unique in that it describes and analyzes impacts across multiple dimensions and puts them in the context of contemporary literature on disaster studies, economic analysis, and the history of science, all based on extensive archival research. The paper concludes by positing that the policy, technical and economic response to the Long Beach earthquake represented a sort of “high modern” example of socially and institutionally constructed “disaster” that firmly set in place the notion that “natural disaster” could be managed and ultimately prevented by material and technical means. It is argued that such a view is still contained within more current and broader concepts of “Resilience” and “Anti-fragility”. Full article

This study examines the macroeconomic impacts of major earthquakes in Türkiye using annual data from 1990 to 2023. Despite growing global interest in disaster economics, evidence on how large seismic events shape national economic performance over extended periods remains limited, particularly in emerging economies. Using data from the World Bank, the Central Bank of the Republic of Türkiye, and the Disaster and Emergency Management Authority, the analysis incorporates real gross domestic product, gross fixed capital formation, consumer prices, and export capacity. A dummy variable identifies years with high-fatality earthquakes. After confirming stationarity, Johansen cointegration and a Vector Error Correction Model were applied. Results indicate that earthquakes exert a statistically significant negative influence on long-term economic growth. Based on the log-level specification, the long-run equilibrium level of real gross domestic product in earthquake years is approximately 45 percent lower than in non-earthquake years. Investment, price stability, and trade capacity support long-term growth. Model diagnostics confirm stability, normality, and no autocorrelation. These findings highlight the structural economic vulnerabilities created by major earthquakes and underscore that disaster risk reduction and resilient infrastructure policies must be integral components of sustainable growth strategies. The study contributes updated national time-series evidence from a structurally fragile context. Full article

Landslides represent a recurrent hazard in tropical mountain environments, where rapid urbanization and extreme rainfall amplify disaster risk. The Sentani region of Papua, Indonesia, is highly vulnerable, as demonstrated by the catastrophic debris flows of March 2019 that caused fatalities and widespread losses. This study developed high-resolution landslide susceptibility maps for Sentani using an ensemble machine learning framework. Three base learners—Random Forest, eXtreme Gradient Boosting (XGBoost), and CatBoost—were combined through a logistic regression meta-learner. Predictor redundancy was controlled using Pearson correlation and Variance Inflation Factor/Tolerance (VIF/TOL). The landslide inventory was constructed from multitemporal satellite imagery, integrating geological, topographic, hydrological, environmental, and seismic factors. Results showed that lithology, Slope Length and Steepness Factor (LS Factor), and earthquake density consistently dominated model predictions. The ensemble achieved the most balanced predictive performance, Area Under the Curve (AUC) > 0.96, and generated susceptibility maps that aligned closely with observed landslide occurrences. SHapley Additive Explanations (SHAP) analyses provided transparent, case-specific insights into the directional influence of key factors. Collectively, the findings highlight both the robustness and interpretability of ensemble learning for landslide susceptibility mapping, offering actionable evidence to support disaster preparedness, land-use planning, and sustainable development in Papua. Full article

Co- and post-seismic earthquake-induced hydrogeological anomalies (EQHAs) in Greece are mainly associated with moderate to strong earthquakes (M_w = 6.0–7.0), particularly when seismic intensities reach IX or above. The highest frequencies are observed in the Peloponnese and Ionian Islands, followed by Central Greece and the North Aegean, characterized by dense faulting and frequent strong earthquakes. EQHAs are classified into six main types, with hydraulic variations being the most common. About 77% of earthquakes produced only one or two types of EQHA, suggesting localized hydrogeological effects, while only a few induced multiple types. Strong events (M_w = 6.0–7.0), often historic, generated the broadest variety, highlighting the influence of local geological, hydrological, and tectonic conditions on magnitude alone. Springs and wells, representing 81% of the cases, dominate the affected systems, while lakes and rivers respond less often but significantly. Most EQHAs occur in Greece’s second seismic hazard zone (74%) due to its larger geographic area. EQHAs primarily develop in karstic and porous formations but also appear in impermeable rocks due to fracturing or karst. Larger earthquakes trigger anomalies at greater distances (>100 km). Though rarely fatal, EQHAs can damage water infrastructure, contaminate supplies, and cause shortages, underscoring the need for systematic monitoring and post-earthquake water resource management. Full article

Earthquakes, as one of the most destructive natural disasters, often cause significant casualties and severe economic losses. Accurate prediction of earthquake fatalities is of great importance for pre-disaster prevention and mitigation planning, as well as post-disaster emergency response deployment. To address the challenges of small sample sizes, high dimensionality, and strong nonlinearity in earthquake fatality prediction, this paper proposes an integrated modeling approach (PCA-IWOA-XGBoost) combining Principal Component Analysis (PCA), the Improved Whale Optimization Algorithm (IWOA), and Extreme Gradient Boosting (XGBoost). The method first employs PCA to reduce the dimensionality of the influencing factor data, eliminating redundant information and improving modeling efficiency. Subsequently, the IWOA is used to intelligently optimize key hyperparameters of the XGBoost model, enhancing the prediction accuracy and stability. Using 42 major earthquake events in China from 1970 to 2025 as a case study, covering regions including the west (e.g., Tonghai in Yunnan, Wenchuan, Jiuzhaigou), central (e.g., Lushan in Sichuan, Ya’an), east (e.g., Tangshan, Yingkou), north (e.g., Baotou in Inner Mongolia, Helinger), northwest (e.g., Jiashi in Xinjiang, Wushi, Yongdeng in Gansu), and southwest (e.g., Lancang in Yunnan, Lijiang, Ludian), the empirical results showed that the PCA-IWOA-XGBoost model achieved an average test set accuracy of 97.0%, a coefficient of determination (R²) of 0.996, a root mean square error (RMSE) and mean absolute error (MAE) reduced to 4.410 and 3.430, respectively, and a residual prediction deviation (RPD) of 21.090. These results significantly outperformed the baseline XGBoost, PCA-XGBoost, and IWOA-XGBoost models, providing improved technical support for earthquake disaster risk assessment and emergency response. Full article

Earthquakes are sudden and highly destructive events that severely disrupt infrastructure and logistics systems, making accurate and timely emergency material demand forecasting a critical challenge in disaster response. However, the scarcity of reliable data during the early stages of an earthquake limits the effectiveness of traditional forecasting methods. To address this issue, this study proposes a hybrid forecasting framework that integrates the Grey Model (GM(1,1)) with Bayesian Dynamic Linear Models (BDLMs), aiming to improve both the accuracy and adaptability of demand predictions. The approach operates in two phases: first, GM(1,1) generates preliminary forecasts using limited initial observations; second, BDLMs dynamically update these forecasts in real time as new data become available. The model is validated through a case study of the 2010 M7.1 Yushu earthquake in Qinghai Province, China. The results indicate that the hybrid method produces reliable forecasts even at the earliest stages of the disaster, with increasing accuracy as more observational data are incorporated. Our case study demonstrates that the integrated GM(1,1)-BDLM framework substantially reduces prediction errors compared to standalone GM(1,1). Using the first five days’ data to forecast fatalities and emergency material demand for days 6–10, the hybrid model achieves a 4.01% error rate—a 19.62 percentage point improvement over GM(1,1)’s 23.63% error rate. This adaptive forecasting mechanism offers robust support for evidence-based decision-making in emergency material allocation, enhancing the efficiency and responsiveness of post-disaster relief operations. Full article

This study presents numerical simulations of the Aysén tsunami, which occurred on 21 April 2007. The tsunami was triggered by hundreds of landslides caused by a magnitude 6.2 earthquake. With an estimated wave height of 50 m at the northern tip of the Mentirosa Island, the event resulted in 10 fatalities and the destruction of multiple salmon farms along the fjord. We employed the NHWAVE and FUNWAVE-TVD numerical software to conduct a series of simulations using various landslide configurations and two approaches to model landslide motion: a viscous flow and a solid slide governed by Coulomb friction. The numerical results indicate that the solid landslide model without basal friction provides the most accurate representation of the measured in situ run-up heights and generates the largest inundation areas. Furthermore, the simulation results show that the arrival time of the tsunami waves was approximately 600 s. Our findings indicate that the volume of the landslide is the most critical factor in determining tsunami wave heights. Additionally, the Coulomb friction angle is another significant parameter to consider in the modeling process. Full article

The increasing frequency and destructiveness of natural disasters necessitate scalable, transparent, and timely analytical frameworks for risk reduction. Traditional disaster datasets—curated by intergovernmental bodies such as EM-DAT and UNDRR—face limitations in spatial granularity, temporal responsiveness, and accessibility. This study addresses these limitations by introducing a novel, AI-enhanced disaster intelligence framework that leverages 19,130 publicly available news articles from 453 global sources between September 2023 and March 2025. Using OpenAI’s GPT-3.5 Turbo model for disaster classification and metadata extraction, the framework transforms unstructured news text into structured variables across five key dimensions: severity, location, media coverage, economic resilience, and casualties. Hypotheses were tested using statistical modeling, geospatial aggregation, and time series analysis. Findings confirm a modest but significant correlation between severity and casualties ($\rho =0.12$, $p<{10}^{-60}$), and a stronger spatial correlation between average regional severity and impact ($\rho =0.31$, $p<{10}^{-10}$). Media amplification bias was empirically demonstrated: hurricanes received the most coverage (5599 articles), while under-reported earthquakes accounted for over 3 million deaths. Economic resilience showed a statistically significant but weak protective effect on fatalities ($\beta =-0.024$, $p=0.041$). Disaster frequency increased substantially over time (slope ${\eta }_1=53.17$, $R^2=0.32$), though severity remained stable. GPT-based classification achieved a high average F1-score (0.91), demonstrating robust semantic accuracy, though not mortality prediction. This study validates the feasibility of using AI-curated, open-access news data for empirical hypothesis testing in disaster science, offering a sustainable alternative to closed datasets and enabling real-time policy feedback loops, particularly for vulnerable, data-scarce regions. Full article

In subtropical regions, typhoons and tropical storms can generate massive rainstorms resulting in thousands of landslides, often termed as Multiple-Occurrence of Regional Landslide Events (MORLE). Understanding the hazards, their location, and their triggering mechanism can help to mitigate exposure and potential impacts. Extreme rainfall events and earthquakes frequently trigger destructive landslides that cause extensive economic loss, numerous fatalities, and significant damage to natural resources. However, inventories of rainfall-induced landslides suggest that they occur frequently under climate change. This study proposed a semi-automated time series algorithm that integrates Sentinel-2 and Integrated Multi-satellite Retrievals for Global Precipitation Measurements (GPM-IMERG) data to detect rainfall-induced landslides. Pixel-wise NDVI time series data are analyzed to detect change points, which are typically associated with vegetation loss due to landslides. These NDVI abrupt changes are further correlated with the extreme rainfall events in the GPM-IMERG dataset, within a defined time window, to detect RIL. The algorithm is tested and evaluated eight previously published landslide inventories, including both those manually mapped and those derived from high-resolution satellite data. The landslide detection yielded an overall F1-score of 0.82 and a mean producer accuracy of 87%, demonstrating a substantial improvement when utilizing moderate-resolution satellite data. This study highlights the combination of using optical images and rainfall time series data to detect landslides in remote areas that are often inaccessible to field monitoring. Full article

In seismic risk analyses, collapse assessment is of critical importance, as it leads to most injuries and fatalities, as well as significant economic losses. In this paper, the seismic collapse response of some 3D prototypes representative of the 1970s Italian reinforced concrete building stock has been analyzed. The considered prototypes have been selected based on two of the most important typological parameters, namely the number of storeys (three types: 2-, 4-, and 6-storey) and the design level (two types: gravity load design, representative of pre-code types, and earthquake-resistant design with low lateral load intensities without anti-seismic details, representative of low-code types). Incremental non-linear dynamic analyses have been performed along the two in-plane directions using a set of 20 real signals scaled up to collapse. The inter-storey drift ratio values at collapse have been analyzed to estimate the mean and dispersion values of the best-fitting distribution functions. These results can be used as capacity thresholds for assessing seismic performance in numerical analyses. Fragility curves have also been derived using different intensity measures to estimate the exceedance probability of collapse, accounting for their inherent efficiency, to be used in seismic risk analyses. Results have been compared to provide valuable insights into the influence of the considered typological parameters on collapse. Full article

The Kahramanmaraş (Türkiye) earthquake on 6 February 2023, one of the largest earthquakes of the century, caused the collapse or severe damage of thousands of structures. This catastrophic disaster resulted in over 53,000 fatalities and rendered many structures unusable. This study addresses the observed damage in reinforced concrete (RC) structures, which constituted the majority of the existing urban building stock. In this study, firstly, information about the destructive Kahramanmaraş earthquakes was given. The predicted PGAs in the last two earthquake hazard maps used in Türkiye were compared with the measured PGAs from actual earthquakes to determine whether the earthquake hazard is adequately represented for eleven affected provinces in the earthquake region. The damages in RC structures were evaluated within the scope of civil and earthquake engineering. Structural analyses for the model created to represent mid-rise RC buildings in the region were carried out separately for each province using predicted and measured PGAs. Additionally, target displacements that were used in performance-based earthquake engineering for damage prediction, were examined comparatively for all provinces. While the predicted earthquake hazard and targeted displacements were exceeded in some provinces, there was no exceedance in the other provinces. The realistic representation of earthquake hazards will allow the predicted displacements for various performance levels of structures to be determined in a much more realistic way. Consequently, the performance levels predicted for the structures will be assessed with greater accuracy. The study highlights the importance of accurately presenting earthquake hazards to predict building performance effectively. Full article

The city of Aïn Témouchent, located in northwest Algeria at the westernmost part of the Lower Cheliff Basin, has experienced several moderate earthquakes, the most significant of which occurred on 22 December 1999 (Mw 5.7, 25 fatalities, severe damage). In this study, ambient noise measurements from 62 sites were analyzed using the horizontal-to-vertical spectral ratio (HVSR) method to estimate fundamental frequency ($f_0$) and amplitude ($A_0$). The inversion of HVSR curves provided sedimentary layer thickness and shear wave velocity (Vs) estimates. Additionally, four spatial autocorrelation (SPAC) array measurements refined the Rayleigh wave dispersion curves, improving Vs profiles (150–1350 m/s) and sediment thickness estimates (up to 390 m in the industrial zone). Vs30 and vulnerability index maps were developed to classify soil types and assess liquefaction potential within the city. Full article

The Himalayan country of Nepal is vulnerable to landslides, earthquakes, and fires. Its inhabitants need to be empowered on how to react in emergencies to prevent fatalities and respond to crises efficiently while promoting longer-term sustainability and resilience. This research project, a collaborative effort involving teachers, students, and researchers, highlights the design and implementation of games for disaster risk reduction tested in remote schools. Three interactive games were developed using an iterative game design process and testing in workshops aiming to ensure the inclusivity and diversity of the project. The games targeted preparedness and response to landslides, earthquakes, and house fires. The outcome has proven that the game-based approach to teaching and learning is crucial in empowering underserved school children often left out in formal and informal disaster management processes. This study has shown that game-based learning of disaster preparedness and response effectively empowers resource-deficient communities and regions of the Global South. Full article

Despite significant scientific and technological advancements in earthquake engineering, earthquakes continue to cause widespread destruction of the built environment, often resulting in numerous fatalities and substantial economic losses. Southeastern Europe, which includes Croatia, is part of the Mediterranean–Trans-Asian high-seismic activity zone. This area has recently experienced a series of earthquakes which had severe consequences for both populations and economies. Notably, the types of buildings that suffered significant damage or collapse during these events still constitute a large portion of the building stock across the region. The majority of residential buildings in Croatia and neighboring areas was constructed before the adoption of modern seismic standards, indicating that a considerable part of the building stock remains highly vulnerable to earthquakes. Therefore, the main goal of this study is to identify the building types which significantly contribute to seismic risk, with the focus on Zagreb as Croatia’s largest city and the capital; collect the documentation on the structural systems and occupancy; analyze the data; and carry out the initial vulnerability assessment. This serves as a first step toward developing a new exposure and vulnerability model for Zagreb that is also applicable to all urban areas in the region with similar building stock and seismotectonic conditions. Full article