Hazard Mitigation Strategies Research Articles

Explainable Artificial Intelligence (XAI) for Climate Hazard Assessment: Enhancing Predictive Accuracy and Transparency in Drought, Flood, and Landslide Modeling

The integration of Artificial Intelligence (AI) into geosciences has ushered in a transformative era for spatial modeling and climate-induced hazard assessment. This study explores the application of Explainable AI (XAI) to address the inherent limitations of traditional "black-box" AI models, emphasizing transparency and interpretability in high-stakes domains such as natural hazard management. By analyzing hydrometeorological hazards—including droughts, floods, and landslides—this work highlights the growing potential of XAI to improve predictive accuracy and facilitate actionable insights. The research synthesizes advancements in XAI methodologies, such as attention models, Shapley Additive Explanations (SHAP), and Generalized Additive Models (GAM), and their application in spatial hazard prediction and mitigation strategies. Additionally, the study identifies challenges in data quality, model transferability, and real-time explainability, proposing pathways for future research to enhance XAI's utility in decision-making frameworks. This comprehensive overview contributes to bridging gaps in the adoption of XAI, enabling robust, transparent, and ethical approaches to climate hazard assessments in an era of rapid environmental change.

Susceptibility modeling of hydro-morphological processes considered river topology

ABSTRACT Hydro-Morphological Processes (HMP, any natural phenomenon contained within the spectrum defined between debris flows and flash floods) are most likely to occur in small catchments, especially buffer zones along or near rivers. Rivers transfer matter and energy between hydrographic units, thus potentially affecting the occurrence of HMPs in nearby catchments. To date, previous HMP susceptibility studies based on data-driven modeling lacked taking into account these interactions between catchments. In this work, we fully considered the role played by river topology and developed a Topology-based HMP susceptibility model (Topo-HMPSM) to emulate the interactions between catchments and predict the susceptibility of HMPs for the Yangtze River Basin during 1985–2015. Results confirmed that our proposed model outperforms four selected baseline models with the best F1-score (mean = 0.744, best = 0.756) and relatively lower uncertainties. A graph-based deep neural network improves the predictive and interpretability of HMP susceptibility modeling using embedding learning techniques. This work attempts to set a standard for incorporating river topology into deep learning models. Our findings highlight the importance of river topology in predicting HMP and support better informed hazard mitigation strategies.

Spatial distribution and transport characteristics of debris flow sediment using high resolution UAV images in the Ohya debris flow fan

Debris flows are significant geomorphic processes that can pose hazards to human settlements and infrastructure, making the understanding of their sediment transfer characteristics and impacts on the surrounding landscape crucial for effective land management and hazard mitigation strategies. This study investigates the distribution patterns of sandstone, vegetation, and shale classes within the Ohya debris flow fan, aiming to gain insights into the sediment characteristics, grain size variations, and vegetation cover trends along the flow path. Using a combination of field surveys and image processing we analyzed sediment characteristics and grain size variations a three study zones: upstream, transfer, and downstream. Statistical parameters such as mean, mode, skewness, and kurtosis were employed to quantify the grain size distribution and identify variations across the study zones. Results reveal distinct spatial variations in the distribution of sandstone, vegetation, and shale classes along the flow path. We observed a decrease in sandstone and vegetation cover from the upstream to the downstream sections, while the area covered by the shale class exhibited an increasing trend in the downstream direction. These findings suggest that debris flows selectively remove smaller particles downstream. The distribution of particle sizes within the sandstone class also exhibited variations, with a higher proportion of smaller pebbles downstream due to more effective removal of finer particles in the upstream zone. This study highlights the complex interplay between debris flow dynamics, sediment transport, and vegetation colonization. The findings contribute to the scientific knowledge of debris flow processes and can be used to inform land management decisions and mitigate the hazards associated with debris flows in similar environments.

Characterizing interactive compound flood drivers in the Pearl River Estuary: A case study of Typhoon Hato (2017)

Tropical cyclone (TC) induced compound floods involve dynamic interactions among astronomical tides, storm surges, precipitation, and associated river pulses. This study employs a one-way coupled WRF, Delft3D, and HEC-RAS model to investigate the impacts of oceanic, pluvial, and fluvial processes on compound flood dynamics during Typhoon Hato (2017) in the Pearl River Estuary (PRE), South China. Total water levels driven by different combinations of flood drivers are modeled and analyzed. Relative contributions of each type of flood driver are quantified and used to categorize flood zones. This study highlights the persistent impacts of storm surges and their nonlinear interactions with other flood drivers. They can modify both the timing and magnitude of maximum water levels, thereby distorting tidal signals and contributing to post-TC landfall water level peaks. Along coastal regions, water levels exhibit three successive peaks, predominantly driven by storm surge, rainfall, and the combined actions of both factors, respectively. In upstream regions and coastal areas sheltered from islands, a singular water level peak arises exclusively from rainfall-runoff processes. Moreover, nonlinear interactions between surge and rainfall-runoff have non-negligible impacts on the relative contribution of individual flood drivers, which underscores the necessity of considering both rainfall and storm surge in modeling compound flood water levels. During the flooding period, peak storm surge and the following peak rainfall resulted in a time-varying distribution of flood zones. Alternating feedback between compounded and ocean-dominant areas manifests in the midsections of the upper PRE. Maximum flooding depth, extent, and duration are mainly influenced by rainfall. Storm surges play a secondary role, causing intense but short-lived flooding in coastal regions. These findings aid in understanding the generation mechanism of compound floods and provide references for hazard mitigation strategies.

Large-deformation finite-element modeling of seismic landslide runout: 3D probabilistic analysis with cross-correlated random field

Landslides significantly threaten lives and infrastructure, especially in seismically active regions. This study conducts a probabilistic analysis of seismic landslide runout behavior, leveraging a large-deformation finite-element (LDFE) model that accounts for the three-dimensional (3D) spatial variability and cross-correlation in soil strength — a reflection of natural soils' inherent properties. LDFE model results are validated by comparing them against previous studies, followed by an examination of the effects of univariable, uncorrelated bivariable, and cross-correlated bivariable random fields on landslide runout behavior. The study’s findings reveal that integrating variability in both friction angle and cohesion within uncorrelated bivariable random fields markedly influences runout distances when compared with univariable random fields. Moreover, the cross-correlation of soil cohesion and friction angle dramatically affects runout behavior, with positive correlations enlarging and negative correlations reducing runout distances. Transitioning from two-dimensional (2D) to 3D analyses, a more realistic representation of sliding surface, landslide velocity, runout distance and final deposit morphology is achieved. The study highlights that 2D random analyses substantially underestimate the mean value and overestimate the variability of runout distance, underscoring the importance of 3D modeling in accurately predicting landslide behavior. Overall, this work emphasizes the essential role of understanding 3D cross-correlation in soil strength for landslide hazard assessment and mitigation strategies.

Evaluating interseismic deformation patterns in the North Tabriz Fault (Iran) using enhanced fitting of velocity field and analysis of surface deformation

Understanding the mechanisms and locations of interseismic strain accumulation along faults is essential for assessing earthquake hazards. However, the mechanical response during the transition from deep fault locking to creep behavior remains uncertain. Estimating the slip deficit within these transition zones is challenging. This study challenges the assumption of a constant depth distribution of interseismic slip rate along the fault over time and proposes variable locking depths as an alternative model. By rejecting the constant locking depth assumption, singularity issues during stress theorem resolution are resolved. To address this, we employ a methodology considering creep propagation within a fully elastic medium. This approach incorporates long-term deformation resulting from viscoelastic flow in the upper mantle and lower crust. Including viscoelastic effects improves the fit to interseismic deformation rates, yielding lower locking depths compared to fully elastic models. To conduct the investigation, the GPS velocity field is recovered using the forward problem and the boundary element method. Subsequently, a physics-based inversion approach, deep interseismic creep, is employed to determine interseismic deformation patterns on a strike-slip fault. Furthermore, this study examined the correlation between the dislocation parameters and their relationship, as well as established the probability distributions associated with each faulting parameter. This research highlights the importance of considering variable locking depths in understanding interseismic strain accumulation and the transition to creep behavior along faults. The findings contribute to improved earthquake hazard assessment and mitigation strategies by providing valuable insights into fault behavior mechanics along the North Tabriz Fault.

Four years of landslide observation with anthropogenic loading as an additional trigger - Analysis of seasonal and annual variability of physical parameters

Determining the structure and evolution of landslides is crucial for geophysical hazard assessment. In this study, we employed an approach integrating the methodologies of multi-channel analysis of surface waves (MASW) and electrical conductivity to image temporal and spatial changes within a landslide in southern Poland. The area, located in the Outer Carpathians, experiences significant climate fluctuations, compounded by anthropogenic activities such as recreational skiing requiring artificial snow.Our combined seismic and electrical methods techniques reveal the landslide's susceptibility to environmental factors on both annual and seasonal scales. Additional analysis, including data clustering and remote sensing, identifies three distinct landslide zones with varying vulnerability to natural and anthropogenic influences.While focusing on a specific area, our approach has global applicability to similar mass movements. This research addresses a gap in understanding time-dependent geophysical observations of moisture-driven landslides, providing valuable insights for hazard identification and mitigation strategies.

Co-design as participation: Creating meaningful pathways for collaboration in flood risk adaptation in Ohkay Owingeh Pueblo

Considering the detrimental effects caused by wildfires in New Mexico, there is a pressing need for the development of innovative hazard adaptation and mitigation strategies. Community-based approaches in hazard response have the potential to harness the power of co-design and the incorporation of traditional knowledge to help make conventional research and design process considerably less extractive. In this paper, we explore the elements of community based, bottom-up approaches that can support successful outcomes, in contrast to centralized top-down approaches. By offering a critical perspective of the idea that projects defined as bottom-up are inherently more effective than those that are top-down, we aim to amend a common participatory design framework, the double-diamond design method, and present a community-engaged framework on flood risk adaptation with the co-design of an environmental data dashboard to address this gap. This can be mitigated by employing a more human-centered design approach as a baseline, and further amending it to center community. We employed a series of workshops in Ohkay Owingeh Pueblo, in New Mexico, to integrate local knowledge. The goal of these workshops was to design an environmental data dashboard that would eventually serve as a point of information gathering and dissemination for the Pueblo. The workshops featured activities that worked to decolonize the process of co-design and put the Pueblo's needs at the center of the dashboard design process. The workshops allowed for participation in a variety of modalities – visual, verbal, auditory – giving community members space to lead and participate in many different formats.

The Noise Exposure of Urban Rail Transit Drivers: Hazard Classification, Assessment, and Mitigation Strategies

Prolonged exposure to high-intensity noise environments in urban rail transit systems can negatively impact the health and work efficiency of drivers. However, there is a lack of comprehensive understanding of the noise pattern and, therefore, effective mitigation strategies. To control the noise in urban rail transit systems, this study proposes a comprehensive noise assessment framework, including metrics such as average sound pressure level, peak sound pressure level, percentile sound pressure levels, dynamic range, main frequency component, and cumulative time energy to evaluate the noise characteristics. We also employ a density-based spatial clustering of applications with noise (DBSCAN) method to identify the noise patterns with the evaluation of their hazard to urban rail transit drivers. The results have revealed that: (1) The equivalent continuous sound pressure level (Leq) in the cab of Lanzhou Urban Rail Transit Line 1 averages 87.12 dB, with a standard deviation of 8.52 dB, which reveals a high noise intensity with substantial fluctuations. (2) Ten noise patterns were identified, with frequencies varying from 14.47 Hz to 69.70 Hz and Leq varying from 60 dB to 115 dB. (3) The major noise sources from these patterns are inferred to be the train’s mechanical systems, wheel–rail interaction, aerodynamic effects, and braking systems. Combined with the noise patterns and urban rail transit’s operation environment, this study proposes tailored mitigation strategies for applications aimed at protecting drivers’ hearing health, enhancing work efficiency, and ensuring driving safety.

Interorganizational Collaboration for the Implementation of Hazard Mitigation Strategies

Interorganizational Collaboration for the Implementation of Hazard Mitigation Strategies

Dynamic Hazard Assessment of Rainfall-Induced Landslides Using Gradient Boosting Decision Tree with Google Earth Engine in Three Gorges Reservoir Area, China

Rainfall-induced landslides are a major hazard in the Three Gorges Reservoir area (TGRA) of China, encompassing 19 districts and counties with extensive coverage and significant spatial variation in terrain. This study introduces the Gradient Boosting Decision Tree (GBDT) model, implemented on the Google Earth Engine (GEE) cloud platform, to dynamically assess landslide risks within the TGRA. Utilizing the GBDT model for landslide susceptibility analysis, the results show high accuracy with a prediction precision of 86.2% and a recall rate of 95.7%. Furthermore, leveraging GEE’s powerful computational capabilities and real-time updated rainfall data, we dynamically mapped landslide hazards across the TGRA. The integration of the GBDT with GEE enabled near-real-time processing of remote sensing and meteorological radar data from the significant “8–31” 2014 rainstorm event, achieving dynamic and accurate hazard assessments. This study provides a scalable solution applicable globally to similar regions, making a significant contribution to the field of geohazard analysis by improving real-time landslide hazard assessment and mitigation strategies.

Development of smoothed seismicity models for seismic hazard assessment in the Red Sea region

The Red Sea region, situated between the Arabian and African Plates, experiences significant seismic activity due to its tectonic dynamics, with earthquakes ranging from minor to potentially destructive events. This study aims to develop smoothed seismicity models for the region by using an enhanced seismic catalog specific to the Red Sea. This facilitates a detailed spatial and temporal analysis of seismic events, focusing on seismic source characterization essential for probabilistic seismic hazard assessments. A rigorous declustering method excludes foreshocks and aftershocks, focusing on independent seismic events. The analysis uses a spatial grid (0.1∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{\\circ }$$\\end{document} cells in latitude and longitude) to determine seismic event rates, which are then refined using various smoothing techniques. Special attention is given to seismic activity within 0–35 kms of depth, leading to distinct rate models that inform urban development and seismic hazard mitigation strategies in the Red Sea area. These models are crucial for improving resilience, safety, and informed decision-making for urban planning and disaster preparedness, addressing the challenges posed by the region’s tectonic and seismic complexities.

Investigating the reactivation of historical landslides during the 2022 Luding MS6.8 earthquake

On September 5, 2022, a strong earthquake with a magnitude of MS6.8 struck Luding County in Sichuan Province, China, triggering thousands of landslides along the Dadu River in the northwest-southeast (NW-SE) direction. We investigated the reactivation characteristics of historical landslides within the epicentral area of the Luding earthquake to identify the initiation mechanism of earthquake-induced landslides. Records of the two newly triggered and historical landslides were analyzed using manual and threshold methods; the spatial distribution of landslides was assessed in relation to topographical and geological factors using remote sensing images. This study sheds light on the spatial distribution patterns of landslides, especially those that occur above historical landslide areas. Our results revealed a similarity in the spatial distribution trends between historical landslides and new ones induced by earthquakes. These landslides tend to be concentrated within a range of 0.2 km from the river and 2 km from the fault. Notably, both rivers and faults predominantly influenced the reactivation of historical landslides. Remarkably, the reactivated landslides are characterized by their small to medium size and are predominantly situated in historical landslide zones. The number of reactivated landslides surpassed that of previously documented historical landslides within the study area. We provide insights into the critical factors responsible for historical landslides during the 2022 Luding earthquake, thereby enhancing our understanding of the potential implications for future co-seismic hazard assessments and mitigation strategies.

Flow Simulation-Based Methodology for Reducing The Risk of Fuel Fire In An Aircraft’s Fuel System Enclosure

ABSTRACT This study applies advanced computational fluid dynamics (CFD) simulations to enhance the safety of an aircraft’s fuel system by striving to minimize the risk of on-board fire hazards. Employing a detailed flow simulation model, this research assesses the efficacy of newly designed measures within a fuel system, designed for a modified radial engine on a test-bed AN 2 airplane. The model simulates internal airflow, droplet particle flow, the formation of Eulerian Wall Film, and the vaporization process from the fuel film within the enclosure of the aircraft’s equipment bay. By exploring both the original and modified geometries of this fuel system enclosure, the simulations provide insights into the flowpaths of leaking fuel, the spatial and temporal distribution of fuel vapor concentrations, and the overall effectiveness of design modifications aimed at rapid removal of hazardous substances. Structural improvements, including the addition of strategic ventilation inlets and outlets, are proposed based on the simulation results to ensure rapid dispersion of vapors and minimal residual fuel, effectively reducing the potential for vapor ignition. This study thus underscores the potential of precise CFD modelling in identifying risks and developing robust fire hazard mitigation strategies in aviation fuel systems.

Automatic creation of personalised virtual construction safety training in digital twins

The construction industry faces persistent safety challenges resulting in far too many occupational injuries and fatalities. Even advanced measures like virtual safety training methods still fall short in terms of realism. This paper introduces a pioneering approach that exploits digital twins to create virtual training environments (VTEs) tailored for construction safety. The digital twin for construction safety (DTCS) framework seamlessly integrates building information modelling (BIM) models, schedules, resources and hazard data into a prototype using the game engine Unity. It enables the generation of VTEs with minimal user interaction, empowering safety trainers to define personalised scenarios based on specific project parameters and training requirements tailored to the trainees’ needs. The evaluation of this approach through three distinct real-world projects and a case study with trainees demonstrates its efficacy. The results indicate the ability of the DTCS framework to efficiently create VTEs independently of the project once digital project information is available. Moreover, the framework provides meaningful feedback to the trainees by evaluating the performance using runtime data collection and analysis. The collected data promise use for several applications, but future research must validate their effectiveness. Beyond training, the study highlights the utility of VTEs in improving safety design and hazard mitigation strategies through data-driven insights.

Moral Hazard Behaviors and Mitigation Strategies: A Systematic Review

Moral Hazard Behaviors and Mitigation Strategies: A Systematic Review

Investigation of effects of hazard geometry and mitigation strategies on community resilience under tornado hazards using an Agent-based modeling approach

A large number of communities are impacted annually by the increasing frequency of tornado hazards resulting in damage to the infrastructure as well as disruption of community functions. The effect of the hazard geometry (center and angle of tornado path as well as the tornado width) is studied herein on how it influences the recovery of physical and social systems within the community. Given that pre-disaster preparedness including mitigation strategies (e.g., retrofits) and policies (e.g., insurance) is crucial for increasing the resilience of the community and facilitating a faster recovery process, in this study, the impact of various mitigation strategies and policies on the recovery trajectory and resilience of a typical US community subjected to a tornado is investigated considering different sources of uncertainties. The virtual testbed of Centerville is selected in this paper and is modeled by adopting the Agent-based modeling (ABM) approach which is a powerful tool for conducting community resilience analysis that simulates the behavior of different types of agents and their interactions to capture their interdependencies. The results are presented in the form of recovery time series as well as calculated resilience indices for various community systems (lifeline networks, schools, healthcare, businesses, and households). The results of this study can help deepen our understanding of how to efficiently expedite the recovery process of a community.

Quantifying the compound hazard of freezing rain and wind gusts across CONUS

The co-occurrence of freezing rain, ice accumulation and wind gusts (FZG) poses a significant hazard to infrastructure and transportation. However, quantification of the frequency and intensity of FZG is challenged by the lack of direct icing measurements. In this work, we evaluate and then apply an energy balance model to high-frequency data collected during 2005–2022 to derive hourly ice accumulation at 883 stations across the contiguous USA. These estimates are combined with wind gust observations to compute time series of hourly FZG hazard magnitude using the Sperry–Piltz Ice Accumulation (SPIA) index. Results are evaluated using Storm Reports of damage and economic disruption. The hourly SPIA estimates are also used to (i) derive a geospatial atlas of the hazard including the 50 yr return period event intensities for each US state derived using superstations, and (ii) describe storylines of significant events in terms of meteorological drivers and socioeconomic impacts. The highest values of SPIA during the 18 yr study period occur in a region extending from the Southern Great Plains over the Midwest into the densely populated Northeast. States in these regions also have high 50 yr return period maximum radial ice accumulation of 3–5 cm and co-occurring wind gusts >30 ms−1. These values are comparable to past estimates for the 500 yr event which may imply this hazard has been previously underestimated. This atlas can be used to inform optimal FZG hazard mitigation strategies for each state/region.

Retrieval of Interior Structure of Asteroids with the Low-Frequency Telescope DART

Understanding the internal structure of asteroids is crucial for deciphering their formation and establishing defenses against potential hazards. The Daocheng Radio Telescope (DART), a recently constructed interferometric array designed for low-frequency Sun imaging, presents a promising tool for probing asteroid interiors. With a substantial 1-km array aperture and an equivalent receiving area of approximately 8,850 m 2 , DART plays a vital role in diagnosing asteroid internal structures. This study introduces an electromagnetic wave scattering model tailored to asteroids within DART’s operational frequency range (150 to 450 MHz). Ground-based radar detection can unveil multiple facets of these celestial bodies by leveraging low-frequency waves’ penetrating capabilities and capitalizing on asteroids’ rotational dynamics. Through simulations capturing the characteristics of low-frequency waves traversing a layered model and interacting with internal structures, we propose an electromagnetic scattering model of asteroids. Our results underscore DART’s potential as a crucial instrument for discerning the internal structure of near-Earth objects. We first formulate an asteroid model through celestial impact models, dimensional analysis, and data fitting to achieve this. Subsequently, we derive an electromagnetic scattering model using geometric optics and a propagation model for lossy mediums. Simulations demonstrate that morphology and internal structure dictate the distribution of scattered waves, with forward and backscattered waves providing comprehensive internal structure information over a rotation cycle. Furthermore, we observe that alterations in electromagnetic wave frequency induce changes in the scattering characteristics, prompting the convenience of employing multiple frequencies for retrieving detailed information about an asteroid’s internal medium and structure. This multidimensional approach positions DART as a promising asset in advancing our understanding of asteroid interiors, offering valuable insights for scientific inquiry and hazard mitigation strategies.

Geospatial modeling for enhanced landslide susceptibility mapping in atlas mountains of the northeast of Algeria

This study presents a practical geospatial approach, based on geomatic principles, to create landslide susceptibility maps that meet contemporary landscape and land management priorities. By employing a GIS-based statistical modeling, our methodology seamlessly integrates a wide range of factors including topography, lithology, land use, and precipitation. This comprehensive approach allows for a holistic evaluation of landslide susceptibility. We use two widely recognized multi-criteria techniques, namely the Analytic Hierarchy Process (AHP) and the Fuzzy Logic Ratio (FR), which in result produce two distinct yet complementary landslide susceptibility maps (LSMs). The creation of these LSMs relies on a carefully curated dataset of landslides, collected through rigorous analysis of high-resolution satellite imagery, interpretation of aerial photographs, and extensive fieldwork. Eleven key factors are selected to inform the modeling process. To assess the accuracy of the LSMs, we employ ROC curves, with the FR method demonstrating superior predictive performance, achieving an impressive accuracy rate of 75% compared to the AHP model's 65%. These findings highlight the effectiveness of our approach in identifying high landslide susceptibility areas, providing valuable insights for informed land use planning, hazard mitigation strategies, and rapid emergency response measures. The GIS-based statistical modeling technique showcased in this research provides a robust framework for generating precise landslide susceptibility maps in complex mountainous landscapes. This research makes a significant contribution to the evolving field of geomatics, enhancing landscape resilience and promoting sustainable land management practices.