Hazard Assessment Model Research Articles

A Statistical-Based Model for Typhoon Rain Hazard Assessment

Extreme typhoon rainfall can lead to damaging floods near the coastal region in mainland China. In the present study, we calibrate the parameters for a parametric hurricane rain model by using the precipitation radar (PR) data from the Tropical Rainfall Measuring Mission (TRMM) (i.e., PR-TRMM) and the TRMM microwave imager (TMI) data (i.e., TMI-TRMM). To show the applicability of the model for the tropical cyclone (TC) rain hazard assessment, we combine the developed rainfall intensity model with historical and synthetic TC tracks to estimate the T-year return period value of the accumulated rainfall in 24 h, QA24-T. We map QA24-100 for part of the coastal region in mainland China, showing that the spatial variation of QA24-100 is relatively smooth. It was found that the estimated QA24-100 using the model developed, based on the snapshots from PR-TRMM, is about 60% of that obtained using the model developed based on the snapshots from TMI-TRMM. This reflects the differences in the rainfall intensities reported in TMI-TRMM and PR-TRMM. As part of verification, we compare the estimated return period value to that obtained by using the record from surface meteorological stations at a few locations. The comparison indicates that, on average, QA24-100 based on gauge data is about 1.4 and 2.3 times that obtained using the model developed based on the snapshots from PR-TRMM and TRM-TRMM, respectively. This suggests that, for TC rain hazard estimation, one may consider the empirical scaling factor of 1.4 and 2.4 for the rainfall intensity models developed based on snapshots from PR-TRMM and TMI-TRMM, respectively.

A development of a graph-based ensemble machine learning model for skin sensitization hazard and potency assessment.

Many defined approaches (DAs) for skin sensitization assessment based on the adverse outcome pathway (AOP) have been developed to replace animal testing because the European Union has banned animal testing for cosmetic ingredients. Several DAs have demonstrated that machine learning models are beneficial. In this study, we have developed an ensemble prediction model utilizing the graph convolutional network (GCN) and machine learning approach to assess skin sensitization. The model integrates in silico parameters and data from alternatives to animal testing of well-defined AOP to improve DA predictivity. Multiple ensemble models were created using the probability produced by the GCN with six physicochemical properties, direct peptide reactivity assay, KeratinoSens™, and human cell line activation test (h-CLAT), using a multilayer perceptron approach. Models were evaluated by predicting the testing set's human hazard class and three potency classes (strong, weak, and non-sensitizer). When the GCN feature was used, 11 models out of 16 candidates showed the same or improved accuracy in the testing set. The ensemble model with the feature set of GCN, KeratinoSens™, and h-CLAT produced the best results with an accuracy of 88% for assessing human hazards. The best three-class potency model was created with the feature set of GCN and all three assays, resulting in 64% accuracy. These results from the ensemble approach indicate that the addition of the GCN feature could provide an improved predictivity of skin sensitization hazard and potency assessment.

Great earthquake and tsunami potential in the eastern Makran subduction zone: New insights from geodetic and structural constraints

The Makran subduction zone ranks one of the highest potentials for seismic and tsunami hazards. However, the rupture style and tsunami-wave dynamics remain ambiguous due to incomplete historical recordings and the lack of modern seismo-geodetic measurements. In this study, we integrate geomorphological, seismic structural models based on seismic images, InSAR measurement, high-resolution multibeam bathymetry, and the Slab-2 model of the subducted oceanic lithosphere to propose a series of geodetic-structural-constrained slip deficit models for tsunami hazard assessment in the northwestern Indian Ocean with a focus on Gwadar port in Pakistan. We show that the accumulated strain on the megathrust could generate an Mw 8.1–8.4 earthquake assuming current steady-state coupling ratio and a 30 GPa rigidity. We reveal the imbricate thrusts of the outer wedge are more efficient to excite tsunamis than the sub-horizontal megathrust (reference model) by increase in wave height at least 1 m (50%) when included, and these have not been considered in tsunami hazard assessments previously. In the worst-case of Mw 9 megathrust model, can occur waves >5 m, strong currents reaching 6 m/s, and inundation distance (>1 km) in the Gwadar port when rupturing to the trench. Additionally, if the updip area of the 1945 event source region ruptured, it could amplify wave height by 0.2-1 m in the Gwadar port and generate a strong tsunami current (>2 m/s) compared with the reference model. These new findings are crucial for seismic and tsunami hazard mitigation and preparedness in the northwestern Indian Ocean. We also demonstrate that modeled maximum tsunami wave heights based on the multibeam, SRTM15+ and GEBCO_2020 bathymetry differ by 0.2-1 m in the source and by 0.1–0.3 m in the far-field. Our study provides better-constrained source models for inundation studies at coastal mega-cities for hazard preparedness and highlights that the tsunami hazard potential is notably higher than previously recognized in this region.

Risk Assessment of Debris Flow in a Mountain-Basin Area, Western China

Debris flow risk comprehensively reflects the natural and social properties of debris flow disasters and is composed of the risk of the disaster-causing body and the vulnerability of the carrier. The Bailong River Basin (BRB) is a typical mountainous environment where regional debris flow disasters occur frequently, seriously threatening the lives of residents, infrastructure, and regional ecological security. However, there are few studies on the risk assessment of mountainous debris flow disasters in the BRB. By considering a complete catchment, based on remote sensing and GIS methods, we selected 17 influencing factors, such as area, average slope, lithology, NPP, average annual precipitation, landslide density, river density, fault density, etc. and applied a machine learning algorithm to establish a hazard assessment model. The analysis shows that the Extra Trees model is the most effective for debris flow hazard assessments, with an accuracy rate of 88%. Based on socio-economic data and debris flow disaster survey data, we established a vulnerability assessment model by applying the Contributing Weight Superposition method. We used the product of debris flow hazard and vulnerability to construct a debris flow risk assessment model. The catchments at a very high-risk were distributed mainly in the urban area of Wudu District and the northern part of Tanchang County, that is, areas with relatively dense economic activities and a high disaster frequency. These findings indicate that the assessment results provide scientific support for planning measures to prevent or reduce debris flow hazards. The proposed assessment methods can also be used to provide relevant guidance for a regional risk assessment of debris flows in the BRB and other regions.

Testing Site Amplification Curves in Hybrid Broadband Ground Motion Simulations of M6.0, 24 August 2016 Amatrice Earthquake, Italy

This research focuses on predicting and assessing earthquake impact due to future scenarios regarding the ground motion seismic hazard by accounting mainly for site effect in the Central Apennines. To this end, we produced synthetic broadband seismograms by adopting a hybrid simulation technique for the Mw6.0 Amatrice earthquake, Central Italy, on 24 August 2016, accounting for site conditions by means of amplification curves, computed with different approaches. Simulations were validated by comparing with data recorded at 57 strong-motion stations, the majority installed in urban areas. This station sample was selected among stations recording the Amatrice earthquake within an epicentral distance of 150 km and potentially prone to experience site amplification effects because of lying in particular site conditions (sedimentary basins, topographic irregularities, and fault zones). The evaluation of amplification curves best suited to describe local effects is of great importance because many towns and villages in central Italy are built in very different geomorphological conditions, from valleys and sedimentary basins to topographies. In order to well reproduce observed ground motions, we accounted for the site amplification effect by testing various generic and empirical amplification curves such as horizontal-to-vertical spectral ratios (calculated from Fourier spectra using both earthquake, HVSR, and ambient noise, HVNSR, recordings) and those derived from the generalized inversion technique (GIT). The site amplifications emanated from GIT improve the match between observed and simulated data, especially in the case of stations installed in sedimentary basins, where the empirical amplification curve effectively reproduces spectral peaks. On the contrary, the worst performances are for the spectral ratios between components, even compared to the generic site amplification, although the latter ignores the strong bedrock/soil seismic impedance contrasts. At sites on topography, we did not observe any systematic behavior, the use of empirical curves ameliorating the fit only in a small percentage of cases. These results may provide a valuable framework for developing ground motion models for earthquake seismic hazard assessment and risk mitigation, especially in urban areas located in the seismically active central Italy region.

Risk Assessment of Tsunamis Along the Chinese Coast Due to Earthquakes

China’s coastal areas are densely populated, economically developed, and located in close proximity to several potential tsunami sources; therefore, tsunami risk cannot be ignored. This study assessed tsunami risk in coastal areas of China by developing a framework for tsunami risk assessment from the perspectives of hazards, vulnerability, and exposure. First, a probabilistic tsunami hazard assessment (PTHA) model was applied to estimate the potential tsunami sources in both local crustal faults and circum-Pacific subduction zones based on numerical simulations. The output of the PTHA includes tsunami wave height distributions along the coast. Then, an indicator system reflecting exposure and vulnerability to tsunamis in the coastal areas of China was established by using the entropy method and analytic hierarchy process. The PTHA findings show that the tsunami wave height is close to 3 m on the southern coast of the Bohai Sea, the Pearl River Estuary, and the Yangtze River Delta and exceeds 2 m near the Taiwan Strait for the 2000-year return period. The results of the tsunami risk assessment show that the cities at the highest risk level (level I) include Tangshan, Yantai, and Hong Kong, while cities at the high risk level (level II) include Fuzhou, Xiamen, and Quanzhou near the Taiwan Strait and many cities on the Yangtze River Delta, the Pearl River Estuary, and the southern coast of the Bohai Sea. Our findings can provide an understanding of differences in tsunami risk between Chinese coastal cities that may be affected by tsunamis in the future.

Gut microbiome and plasma metabolome changes in rats after oral gavage of nanoparticles: sensitive indicators of possible adverse health effects

BackgroundThe oral uptake of nanoparticles is an important route of human exposure and requires solid models for hazard assessment. While the systemic availability is generally low, ingestion may not only affect gastrointestinal tissues but also intestinal microbes. The gut microbiota contributes essentially to human health, whereas gut microbial dysbiosis is known to promote several intestinal and extra-intestinal diseases. Gut microbiota-derived metabolites, which are found in the blood stream, serve as key molecular mediators of host metabolism and immunity.ResultsGut microbiota and the plasma metabolome were analyzed in male Wistar rats receiving either SiO2 (1000 mg/kg body weight/day) or Ag nanoparticles (100 mg/kg body weight/day) during a 28-day oral gavage study. Comprehensive clinical, histopathological and hematological examinations showed no signs of nanoparticle-induced toxicity. In contrast, the gut microbiota was affected by both nanoparticles, with significant alterations at all analyzed taxonomical levels. Treatments with each of the nanoparticles led to an increased abundance of Prevotellaceae, a family with gut species known to be correlated with intestinal inflammation. Only in Ag nanoparticle-exposed animals, Akkermansia, a genus known for its protective impact on the intestinal barrier was depleted to hardly detectable levels. In SiO2 nanoparticles-treated animals, several genera were significantly reduced, including probiotics such as Enterococcus. From the analysis of 231 plasma metabolites, we found 18 metabolites to be significantly altered in Ag-or SiO2 nanoparticles-treated rats. For most of these metabolites, an association with gut microbiota has been reported previously. Strikingly, both nanoparticle-treatments led to a significant reduction of gut microbiota-derived indole-3-acetic acid in plasma. This ligand of the arylhydrocarbon receptor is critical for regulating immunity, stem cell maintenance, cellular differentiation and xenobiotic-metabolizing enzymes.ConclusionsThe combined profiling of intestinal microbiome and plasma metabolome may serve as an early and sensitive indicator of gut microbiome changes induced by orally administered nanoparticles; this will help to recognize potential adverse effects of these changes to the host.

Site specific probabilistic seismic hazard model for Isfahan, Iran: estimates and uncertainties

We present a seismic source characterization model for the probabilistic seismic hazard assessment (PSHA) of the Isfahan urban area, Iran. We compiled the required datasets including the earthquake catalogue and the geological and seismotectonic structure and faults systems within the study region to delineate and characterize seismic source models. We identified 7 relatively large zones that bound each region with similar seismotectonic characteristics and catalogue completeness periods. These regions were used for calculating the b-value of the Gutenberg–Richter magnitude recurrence relationship and for estimating the maximum magnitude value within each region. The earthquake recurrence parameters were then used to build a spatially varying distributed earthquake rate model using a smoothed kernel. Additionally, based on a fault database developed in this study and on a local expert’s opinion supported by tenable constraints about their slip velocity, a fault-based model is also created. We further performed sets of sensitivity analyses to find stable estimates of the ground motion intensity and to define alternative branches for both the seismogenic source and ground motion prediction models. Site amplification is considered based on a Vs30 map for Isfahan compiled within this study. The alternative source and ground motion prediction models considered in the logic tree are then implemented in the software OpenQuake to generate hazard maps and uniform hazard spectra for return periods of interest. Finally, we provide a detailed comparison of these PSHA outcomes with both those presented in the 2014 Earthquake Model of Middle East (EMME14) and with the national seismic design spectrum to further discuss the discrepancies between hazard estimates from site-specific and regional PSHA studies.

Survival of murine norovirus and hepatitis A virus in bottled drinking water, strawberries, and oysters

The survival of murine norovirus (MNV) and hepatitis A virus (HAV) in bottled drinking water, strawberries, and oysters stored at different temperatures for various durations was investigated. In drinking water, over 6.4 and 1.6 log 10 plaque-forming units (PFU/mL) reductions of viable MNV and HAV were observed after 20 days of incubation at 35 °C. After 160 days of incubation at 20 °C, there was a sharp decrease in the MNV (>6.4 log 10 PFU/mL) and HAV (>2.9 log 10 PFU/mL) load but only a slight reduction at 4 °C. In fresh strawberry, the MNV and HAV titers reduced by 1.0 and 0.9 log 10 PFU/g after incubation for 2 days at 25 °C, by 0.85 and 1.22 log 10 PFU/g after 3 days of incubation at 15 °C, and by 0.7 and 1.1 log 10 PFU/g after 7 days of incubation at 4 °C, respectively. After 3 days of incubation at 4, 15, and 25 °C in oyster reductions of over 0.3, 0.4, and 0.6 log 10 PFU/mL in the MNV load and 0.16, 0.28, and 0.52 log 10 PFU/mL in the HAV load were observed, respectively. Even though, survival of the two viruses fluctuated with temperature and food commodity. Therefore, our results indicate that major enteric foodborne viruses such as MNV and HAV can survive for long periods, with only a partial reduction in their titers. Owing to the persistence of foodborne viruses, preventive measures need to be taken to prevent food contamination. • MNV and HAV survival in drinking water, strawberries, and oysters was investigated. • MNV and HAV survivability varies with temperature and food type. • Enteric foodborne viruses can persist for a long time at 4 °C. • Study results could lay the foundation for the development of food hazard assessment models.

Assessment of Logistic Regression Model Performance and Physical Controls on January 9, 2018, Debris Flows, Thomas Fire, California

ABSTRACT The 2017–2018 Thomas Fire burned 281,893 acres of land in southeastern Santa Barbara County and southwestern Ventura County. An atmospheric river storm impacted the region on January 9, 2018, producing intense rainfall in the western and northern portions of the burned area and triggering numerous post-fire debris flows (PFDFs). The most destructive and deadly flows inundated the town of Montecito, where 23 people died. Debris flow source and inundation mapping data across the fire provide a rare opportunity to assess the interplay between rainfall intensity, watershed characteristics, geologic conditions, and resulting PFDF occurrence. Mapped data are compared to spatially explicit analyses of 857 drainage basins modeled with the U.S. Geological Survey (USGS) logistic regression model (LRM) for PFDF prediction using 15-minute rainfall thresholds at 50 and 90 percent (P50 and P90) probabilities of exceedance. Results indicate that the LRM successfully predicted nearly every PFDF reaching the basin pour point. However, overall model accuracy was lowered by numerous false-positive responses, even where rainfall depths were far above LRM thresholds. Analyses of basins where rainfall was above P50 thresholds reveal a strong correlation between high false-positive responses and basins experiencing rainfall of less than about 150 to 200 percent of USGS thresholds. These false positives occurred in basins with small (0.02–0.05 km2), steep (≥23°) burned areas and in basins underlain by relatively weak geologic units that weather to produce few boulders. Identified relationships provide a basis for refining and improving existing PFDF hazard assessment modeling.

An InSAR and depth-integrated coupled model for potential landslide hazard assessment

An InSAR and depth-integrated coupled model for potential landslide hazard assessment

Physical process-based runout modeling and hazard assessment of catastrophic debris flow using SPH incorporated with ArcGIS: A case study of the Hongchun gully

Catastrophic debris flows frequently occur in mountainous areas after an earthquake owing to a substantially increased amount of loosened co-seismic material. Debris flows pose a serious challenge for the reconstruction of earthquake-stricken areas because of their potential reoccurrence over the course of several years after a given earthquake event. It is therefore essential to clarify the dynamics of large-scale debris flows and accurately assess the potential hazard risks. This study selected the Hongchun gully debris flow that occurred on August 14, 2010 in the areas affected by the Wenchuan earthquake in southwestern China as a typical case to analyze the formation conditions and physical processes of catastrophic debris flows. The smoothed particle hydrodynamics (SPH) method was used to reconstruct the physical process of the Hongchun gully debris flow across 3D terrain. The SPH numerical results were integrated into the ArcGIS platform to perform further spatial analysis of the hazard intensity and identify the hazard risk level. The calculated accumulated hazard area agrees well with the field data. By introducing a frequency-magnitude empirical formula, a hazard assessment model of debris flows is proposed based on flow depth and momentum, and applied to predict the potential risk of the Hongchun gully debris flow under different recurrence periods. This work provides a new method for performing hazard assessments of debris flows, and also serves as a guide for reconstruction planning in earthquake-hit areas.

Seismic zoning methods and seismic hazard assessments

The paper considers methods and models for seismic hazard assessment and features of seismic monitoring in Central Siberia. The article is a continuation of the work on the study of algorithmic support for performing a probabilistic seismic hazard analysis (PSHA) as one of the main stages in engineering seismological surveys. The paper presents an overview of modern PSHA approaches based on the Monte Carlo method. The technique for constructing maps of general seismic zoning is described in detail. Examples of construction and comparison of GSZ-2015 and GSZ-2016 maps are given. This work is supported by the Krasnoyarsk Mathematical Center and financed by the Ministry of Science and Higher Education of the Russian Federation in the framework of the establishment and development of regional Centers for Mathematics Research and Education (Agreement No. 075-02-2022-873).

In Silico Methods for Environmental Risk Assessment: Principles, Tiered Approaches, Applications, and Future Perspectives.

This chapter aims to introduce the reader to the basic principles of environmental risk assessment of chemicals and highlights the usefulness of tiered approaches within weight of evidence approaches in relation to problem formulation i.e., data availability, time and resource availability. In silico models are then introduced and include quantitative structure-activity relationship (QSAR) models, whichsupport filling data gaps when no chemical property or ecotoxicological data are available. In addition, biologically-based models can be applied in more data rich situations and these include generic or species-specific models such as toxicokinetic-toxicodynamic models, dynamic energy budget models, physiologically based models, and models for ecosystem hazard assessment i.e.species sensitivity distributionsand ultimately for landscape assessment i.e.landscape-based modeling approaches. Throughout this chapter, particular attention is given to provide practical examples supporting the application ofsuch in silico models in real-world settings. Future perspectives are discussed to address environmental risk assessment in a more holistic manner particularly for relevant complex questions, such as the risk assessment of multiple stressors and the development of harmonized approaches to ultimately quantify the relative contribution and impactof singlechemicals, multiple chemicals and multiple stressors on living organisms.

Occupational risk of nano-biomaterials: Assessment of nano-enabled magnetite contrast agent using the BIORIMA Decision Support System

The assessment of the safety of nano-biomedical products for patients is an essential prerequisite for their market authorization. However, it is also required to ensure the safety of the workers who may be unintentionally exposed to the nano-biomaterials (NBMs) in these medical applications during their synthesis, formulation into products and end-of-life processing and also of the medical professionals (e.g., nurses, doctors, dentists) using the products for treating patients. There is only a handful of workplace risk assessments focussing on NBMs used in medical applications. Our goal is to contribute to increasing the knowledge in this area by assessing the occupational risks of magnetite (Fe3O4) nanoparticles coated with PLGA-b-PEG-COOH used as contrast agent in magnetic resonance imaging (MRI) by applying the software-based Decision Support System (DSS) which was developed in the EU H2020 project BIORIMA.The occupational risk assessment was performed according to regulatory requirements and using state-of-the-art models for hazard and exposure assessment, which are part of the DSS. Exposure scenarios for each life cycle stage were developed using data from literature, inputs from partnering industries and results of a questionnaire distributed to healthcare professionals, i.e., physicians, nurses, technicians working with contrast agents for MRI. Exposure concentrations were obtained either from predictive exposure models or monitoring campaigns designed specifically for this study. Derived No-Effect Levels (DNELs) were calculated by means of the APROBA tool starting from in vivo hazard data from literature. The exposure estimates/measurements and the DNELs were used to perform probabilistic risk characterisation for the formulated exposure scenarios, including uncertainty analysis. The obtained results revealed negligible risks for workers along the life cycle of magnetite NBMs used as contrast agent for the diagnosis of tumour cells in all exposure scenarios except in one when risk is considered acceptable after the adoption of specific risk management measures. The study also demonstrated the added value of using the BIORIMA DSS for quantification and communication of occupational risks of nano-biomedical applications and the associated uncertainties.

A combined approach for flood hazard assessment using hydrological, hydraulic and GIS modeling case study of El Hamiz Wadi, Algeria

Flood hazard is one of the most destructive natural phenomena causing a significant material damage and human losses in Algeria during the past decade. The aim of this study is to assess and characterize the flood hazard in El Hamiz watershed (Algeria). In addition, this study identifies areas and elements prone to this hazard to minimize the damages and losses. The methodology used in the present study is based on a combination of hydrological and hydraulic modeling using the Hec-Ras software and mapping techniques using a Geographic Information System (GIS). The flood hazard assessment model of El Hamiz wadi using different hydrological (rainfall / runoff transfer and return period), hydraulic (water level and flow velocity) and exposure (land use data and socio-economic data) parameters revealed that the downstream part of the studied area is the most susceptible to flood events. The mapping of flood-prone areas indicated the extent of this phenomena on the El Hamiz wadi banks, especially through heavy submersion of residential areas characterized by a high urban concentration including inhabitants, activities and infrastructures.

Ingested Engineered Nanomaterials Affect the Expression of Mucin Genes-An In Vitro-In Vivo Comparison.

The increasing use of engineered nanomaterials (ENM) in food has fueled the development of intestinal in vitro models for toxicity testing. However, ENM effects on intestinal mucus have barely been addressed, although its crucial role for intestinal health is evident. We investigated the effects of ENM on mucin expression and aimed to evaluate the suitability of four in vitro models of increasing complexity compared to a mouse model exposed through feed pellets. We assessed the gene expression of the mucins MUC1, MUC2, MUC5AC, MUC13 and MUC20 and the chemokine interleukin-8 in pre-confluent and confluent HT29-MTX-E12 cells, in stable and inflamed triple cultures of Caco-2, HT29-MTX-E12 and THP-1 cells, and in the ileum of mice following exposure to TiO2, Ag, CeO2 or SiO2. All ENM had shared and specific effects. CeO2 downregulated MUC1 in confluent E12 cells and in mice. Ag induced downregulation of Muc2 in mice. Overall, the in vivo data were consistent with the findings in the stable triple cultures and the confluent HT29-MTX-E12 cells but not in pre-confluent cells, indicating the higher relevance of advanced models for hazard assessment. The effects on MUC1 and MUC2 suggest that specific ENM may lead to an elevated susceptibility towards intestinal infections and inflammations.

Rapid prediction of peak storm surge from tropical cyclone track time series using machine learning

Rapid and accurate prediction of peak storm surges across an extensive coastal region is necessary to inform assessments used to design the systems that protect coastal communities’ life and property. Significant advances in high-fidelity, physics-based numerical models have been made in recent years, but use of these models for probabilistic forecasting and probabilistic hazard assessment is computationally intensive. Several surrogate modeling approaches based on existing databases of high-fidelity synthetic storm surge simulations have been recently suggested to reduce computational burden without substantial loss of accuracy. In these previous studies, however, the surrogate modeling approaches relied on a tropical cyclone condition at one moment (usually at or near landfall), which is not always most correlated with the peak storm surge. In this study, a new one-dimensional convolutional neural network model combined with principal component analysis and a k-means clustering (C1PKNet) is presented that can rapidly predict peak storm surge across an extensive coastal region from time-series of tropical cyclone conditions, namely the storm track. The C1PKNet model was trained and cross-validated for the Chesapeake Bay area of the United States using existing database of 1031 high-fidelity storm surge simulations, including both landfalling and bypassing storms. Moreover, the performance of the C1PKNet model was evaluated based on observations from three historical hurricanes (Hurricane Isabel in 2003, Hurricane Irene in 2011, and Hurricane Sandy in 2012). The results indicate that the C1PKNet model is computationally efficient and can predict peak storm surges from realistic tropical cyclone track time-series. We believe that this new surrogate model can enhance coastal resilience by providing rapid storm surge predictions.

Multiclassification Method of Landslide Risk Assessment in Consideration of Disaster Levels: A Case Study of Xianyang City, Shaanxi Province

Geological disaster risk assessment can quantitatively assess the risk of disasters to hazard-bearing bodies. Visualizing the risk of geological disasters can provide scientific references for regional engineering construction, urban planning, and disaster prevention and mitigation. There are some problems in the current binary classification landslide risk assessment model, such as a single sample type, slow multiclass classification speed, large differences in the number of positive and negative samples, and large errors in classification results. This paper introduces multilevel landslide hazard scale samples, selects multiple types of samples according to the divided multilevel landslide hazard scale grade, and proposes a landslide hazard assessment model based on a multiclass support vector machine (SVM). Due to the objective limitations of the single weighting method, the combined weights are used to determine the vulnerability of the landslide hazard-bearing body, and the analytic hierarchy process (AHP) and entropy method are combined to construct a landslide vulnerability assessment model that considers subjective and objective weights. This paper takes landslide disasters in Xianyang City, Shaanxi Province, as the research object. Based on the landslide hazard assessment model and the landslide vulnerability assessment model, a landslide risk assessment experiment is carried out. It generates the landslide risk assessment zoning map and summarizes the risk characteristics of landslides in various towns. The experimental results verify the feasibility and effectiveness of the proposed model and provide important decision support for decision makers in Xianyang City.

A Hydrodynamic-Based Robust Numerical Model for Debris Hazard and Risk Assessment

Debris flow simulations are important in practical engineering. In this study, a graphics processing unit (GPU)-based numerical model that couples hydrodynamic and morphological processes was developed to simulate debris flow, transport, and morphological changes. To accurately predict the debris flow sediment transport and sediment scouring processes, a GPU-based parallel computing technique was used to accelerate the calculation. This model was created in the framework of a Godunov-type finite volume scheme and discretized into algebraic equations by the finite volume method. The mass and momentum fluxes were computed using the Harten, Lax, and van Leer Contact (HLLC) approximate Riemann solver, and the friction source terms were calculated using the proposed splitting point-implicit method. These values were evaluated using a novel 2D edge-based MUSCL scheme. The code was programmed using C++ and CUDA, which can run on GPUs to substantially accelerate the computation. After verification, the model was applied to the simulation of the debris flow process of an idealized example. The results of the new scheme better reflect the characteristics of the discontinuity of its movement and the actual law of the evolution of erosion and deposition over time. The research results provide guidance and a reference for the in-depth study of debris flow processes and disaster prevention and mitigation.