Natural Hazards Research最新文献

{"title":"Multicriteria assessment of the response capability of urban emergency shelters: A case study in Beijing","authors":"Yiting Xu ,&nbsp;Wei Wang ,&nbsp;Hong Chen ,&nbsp;Minhao Qu","doi":"10.1016/j.nhres.2024.02.001","DOIUrl":"10.1016/j.nhres.2024.02.001","url":null,"abstract":"<div><p>Urban emergency shelters are pivotal for safety, with effective evaluation criteria enabling decision-makers to identify strengths and weaknesses, facilitating targeted enhancements. This study aims to propose a scientific criteria system for assessing urban emergency shelters on a smaller scale, employing the analytical hierarchy process and multisource data. Considering six key perspectives—site safety, spatial accessibility, demand matching, shelter facilities, operation, administration and maintenance (OA&amp;M), and public awareness—a hierarchical structure of evaluation indicators for assessing the emergency response capability of urban emergency shelters is presented. To demonstrate the practicality and scientific validity of the proposed indicator system, we apply GIS analysis to evaluate the Yuandadu Park Emergency Shelter in Beijing. The findings validate the effectiveness of the indicator system and its potential for assessing individual shelters comprehensively. By understanding the strengths and weaknesses of shelters through this comprehensive assessment, decision-makers can make informed choices to improve overall emergency preparedness and response in urban settings.</p></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"4 2","pages":"Pages 324-335"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666592124000210/pdfft?md5=179255e9b692e421d968ec2379614a4a&pid=1-s2.0-S2666592124000210-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140469883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of surface deformation induced by mining thin coal seam: A case study of Guanshan coalfield in Sichuan","authors":"Wei Cai ,&nbsp;Linyang Li ,&nbsp;Mengming Lin ,&nbsp;Jingyong Wang ,&nbsp;Ping Wang ,&nbsp;Qingmiao Li ,&nbsp;Zhiping Ye ,&nbsp;Jie Zhang ,&nbsp;Jianjun Zhao","doi":"10.1016/j.nhres.2023.09.011","DOIUrl":"10.1016/j.nhres.2023.09.011","url":null,"abstract":"<div><p>The problem of surface deformation caused by coal mining is acute and usually lasts for a long time. During coal mining, the movement of the overlying strata has a broad range of influences, which may cause surface deformation, surface cracking, and damage to structures (buildings). However, continuous deformation monitoring data are often scarce in practice, making it challenging to predict the surface deformation caused by coal mining. In this context, this paper takes Sichuan’s Guanshan coalfield as the research object and proposes a comprehensive method integrating interferometric synthetic aperture radar (InSAR) technology, the FLAC3D finite difference software, and the probability integral method to predict the surface deformation caused by mining a thin seam of coal. The results show that the trend of surface deformation estimated by the numerical simulation agrees well with the results of InSAR data and the probability integral model when using InSAR historical deformation data to invert the parameters of rock mechanics in the numerical simulation, which is beneficial to improving the reliability of the simulation results. The calculations of the probability integral method are close to the predictions of the FLAC3D numerical simulation, and a settlement deformation of 0.68 ​m is expected to occur in the Guanshan coalfield area. The comprehensive prediction method proposed in this paper effectively enhances the accuracy of surface deformation prediction under the action of mining and can provide a reference for predicting the surface subsidence of similar coal mines.</p></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"4 2","pages":"Pages 255-264"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666592123000926/pdfft?md5=358cbe8ec65eccfdb954710befac4677&pid=1-s2.0-S2666592123000926-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134935521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comparative study on data mining models for weather forecasting: A case study on Chittagong, Bangladesh","authors":"Mohammad Sadman Tahsin ,&nbsp;Shahriar Abdullah ,&nbsp;Musaddiq Al Karim ,&nbsp;Minhaz Uddin Ahmed ,&nbsp;Faiza Tafannum ,&nbsp;Mst Yeasmin Ara","doi":"10.1016/j.nhres.2023.12.014","DOIUrl":"10.1016/j.nhres.2023.12.014","url":null,"abstract":"<div><p>The primary focus of this study is to analyze and predict the patterns of this essential feature of the natural world. This study analyses and predicts the daily weather patterns of a specific urban area. This article utilizes weather data over 20 years to analyze the climate patterns of Chittagong city. A total of 12 distinct Data Mining models were employed to predict daily weather patterns. The algorithms can be categorized into three distinct types, namely rules-based, tree-based, and function-based. To evaluate the effectiveness of the models, various performance metrics were computed, including precision, recall, accuracy, F-measure, and the area under the receiver operating characteristic curve (ROC area). Based on the results obtained, it can be concluded that among the 12 algorithms evaluated, J48 exhibits the highest level of performance and accuracy. The J48 classifier demonstrated an accuracy of 82.30%, precision of 82.40%, recall of 82.20%, f-measure of 84.20%, and a ROC area of 97.8%. Furthermore, a comprehensive analysis of the confusion matrix for all twelve algorithms was conducted to facilitate further evaluation.</p></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"4 2","pages":"Pages 295-303"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666592123001373/pdfft?md5=3e3d94ee5ac933049e6eed47b5061ed1&pid=1-s2.0-S2666592123001373-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139019827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multivariate spatial regressions help explain wildfire hot spot intensities in Washington, USA","authors":"Kevin Zerbe,&nbsp;Tim Cook,&nbsp;Audrey Vulcano","doi":"10.1016/j.nhres.2023.11.006","DOIUrl":"10.1016/j.nhres.2023.11.006","url":null,"abstract":"<div><p>Wildfires have become increasingly prevalent in the western United States, posing threats to human communities and the built environment. This study builds upon previous research by investigating the factors influencing wildfire hot spot distribution in Washington State. Using spatial regression models (generalized linear regression and geographically weighted regression), we examine the relationships between wildfire hot spots and various geographic features, including climate variables, human-caused ignitions, land use, population density, road density, and the wildland-urban interface. Our results indicate that lightning-caused fires and road density are significant factors contributing to hot spot intensity in central Washington, while human-caused ignitions play a crucial role in eastern Washington. Surprisingly, precipitation shows varied correlations with hot spots, with some areas experiencing an unexpected positive relationship between precipitation and hot spot intensity due to increased fuel growth. The study highlights the importance of localized approaches to wildfire mitigation, emphasizing the need for tailored risk reduction strategies based on regional factors.</p></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"4 2","pages":"Pages 288-294"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666592123001166/pdfft?md5=e8e812e4f7a947ac09d6e4484a6a65b7&pid=1-s2.0-S2666592123001166-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135664480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Riverbank Erosion and vulnerability – A study on the char dwellers of Assam, India","authors":"Mrinal Saikia,&nbsp;Ratul Mahanta","doi":"10.1016/j.nhres.2023.10.007","DOIUrl":"10.1016/j.nhres.2023.10.007","url":null,"abstract":"<div><p>The paper tries to analyze the impacts of erosion on the livelihood and vulnerability statusof the char dwellers of Assam, India. The study employs both quantitative and qualitative methodologies, choosing one district from each of Assam's agro-climatic zones across the Brahmaputra valley as a representative of the state's char regions. As a qualitative tool, the study uses the participatory rural appraisal (PRA) technique and as quantitative tool the study uses Vulnerability as Uninsured Exposure to Risk (VER) econometric model.394 char households were surveyed through a semi-structured schedule. For each village selected for the study, a combined social-resource map was created using the PRA method in order to determine the severity of the erosion issue in the char regions. The VER model is used to empirically examine the relationship between char land erosion and the well-being of char inhabitants. The study reveals that erosion of the char land has serious, detrimental impacts on the livelihood and economic well-being of the char residents and thereby make the char dwellers vulnerable. The study makes recommendations of both structural and non-structural adaptation practices to minimize the effects of erosion on char dwellers livelihood.</p></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"4 2","pages":"Pages 274-287"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666592123001026/pdfft?md5=0585f7a1e4399370caa95338de46af18&pid=1-s2.0-S2666592123001026-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135668932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Forest fire in Thailand: Spatio-temporal distribution and future risk assessment","authors":"Nazimur Rahman Talukdar ,&nbsp;Firoz Ahmad ,&nbsp;Laxmi Goparaju ,&nbsp;Parthankar Choudhury ,&nbsp;Abdul Qayum ,&nbsp;Javed Rizvi","doi":"10.1016/j.nhres.2023.09.002","DOIUrl":"10.1016/j.nhres.2023.09.002","url":null,"abstract":"<div><p>Understanding the spatiotemporal distribution of forest fires and future predictions is very important for management strategies. To identify the present status of forest fires in the Kingdom of Thailand and their risk in the future, ten-year forest fire data were used, and a forest fire hotspot was prepared. A geospatial technique was used in the study to characterize the parameters of forest fires in the country and identify future forest fire risk areas. Most of the forest fires in the country were found to be seasonal. Deciduous forests in higher elevations and on moderate slopes were most vulnerable to forest fire. The level of aridity, soil moisture, temperature, precipitation, vegetation status, and topography influenced the spatiotemporal distribution of forest fires in the country. Greater than 50% of fire risks were observed in 22 administrative divisions, and 17 of the 209 protected areas are also in the high-risk category. The final forest fire hotspot map can be used in policy development and successful management strategies. A better monitoring strategy should be used in the fire hotspot areas as a precautionary measure to minimize the anthropogenic causes of forest fires.</p></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"4 1","pages":"Pages 87-96"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666592123000847/pdfft?md5=c6482525b0dd78cc35c3feda8093fc9a&pid=1-s2.0-S2666592123000847-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79863220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring potential glacial lakes using geo-spatial techniques in Eastern Hindu Kush Region, Pakistan","authors":"Mariam Sarwar,&nbsp;Shakeel Mahmood","doi":"10.1016/j.nhres.2023.07.003","DOIUrl":"10.1016/j.nhres.2023.07.003","url":null,"abstract":"<div><p>The study aimed to investigate the potential glacial lakes in response to climate change and the associated risk of glacial lake outburst floods (GLOFs). Remote sensing data and GIS techniques were utilized to analyze glacial lakes, employing empirical models to estimate their area, volume, and depth. The Normalized Difference Water Index (NDWI) was applied to detect changes in glacial lakes using Sentinel imagery. The findings revealed a notable increase in both the number and surface area of glacial lakes over the past two decades. Specifically, the number of glacial lakes rose from 101 in 2000 to 162 in 2020, while their combined surface area expanded from 9.72 km² to 12.36 km² during the same period. Among these lakes, 31 were identified as Potentially Dangerous Glacial Lakes (PDGLs), with 6 located in Chitral, 16 in Swat, and 9 in Upper Dir. Two lakes were classified as high potential glacial lakes, with depths estimated at 41.86 ​m and 30.43 ​m. Continued monitoring of these glacial lakes and their susceptibility to GLOFs is crucial in the face of ongoing climate change. Long-term planning and adaptation strategies are necessary to safeguard the well-being and safety of communities residing in these vulnerable regions. By understanding the evolving characteristics of these lakes, researchers and policymakers can better prepare for and mitigate the impacts of GLOFs on downstream communities and infrastructure.</p></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"4 1","pages":"Pages 56-61"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666592123000720/pdfft?md5=89e3d3a45e7be0775a6234461c32f3b2&pid=1-s2.0-S2666592123000720-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81784580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterizing egress components for wheelchair users in dormitory building fires","authors":"Haley Hostetter,&nbsp;M.Z. Naser","doi":"10.1016/j.nhres.2023.11.012","DOIUrl":"10.1016/j.nhres.2023.11.012","url":null,"abstract":"<div><p>People with disabilities are among the most vulnerable groups in building fires. According to the U.S. Fire Administration, an estimated 700 home fires involve people with physical disabilities each year. In parallel, the National Fire Protection Association estimates that 11% of civilian fire deaths were people with disabilities. Despite these statistics, the current body of literature shows few studies focused on the evacuation of disabled people. To bridge this knowledge gap, this paper presents findings on the evacuation processes of wheelchair users in a low-rise apartment (dormitory) building. More specifically, we simulate 1–3 wheelchair users in a dormitory building at our home institution via 327 simulations to examine evacuation time as well as identify structural aids and barriers. As a byproduct of this research, a new dynamic structural ranking system of egress components is proposed for wheelchair users, and a series of suggestions for structural modifications to improve the egressibility of the simulated building are provided.</p></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"4 1","pages":"Pages 173-186"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666592123001221/pdfft?md5=b79c8ede190a2311997b03185cc72417&pid=1-s2.0-S2666592123001221-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138612340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accommodating uncertainty in soil erosion risk assessment: Integration of Bayesian belief networks and MPSIAC model","authors":"Hossein Bashari ,&nbsp;Abdolhossein Boali ,&nbsp;Saeid Soltani","doi":"10.1016/j.nhres.2023.09.009","DOIUrl":"10.1016/j.nhres.2023.09.009","url":null,"abstract":"<div><p>Accommodating uncertainty stands as one of the most salient challenges in the development of soil erosion assessment tools. We presented a novel approach integrating the Modified Pacific Southwest Inter-Agency Committee (MPSIAC) model and Bayesian Belief Networks (BBNs) to assess soil erosion in a region of western Iran. The soil erosion status was reckoned based on the nine factors of MPSIAC. We utilized BBNs to produce a causal model for soil erosion, with output probabilities being validated through re-evaluation and sensitivity analysis. We identified erosion types, geological formations, run-off, soil erodibility, soil permeability, soil characteristics, and precipitation intensity as the main determinants of soil erosion. A significant, positive correlation existed between the erosion rate derived from MPSIAC and BBNs model in all land-use/covers over the work units. Overall, this study highlighted the potential of BBNs as a supportive tool for soil erosion prediction as well as a relatively simple and updatable soil erosion model for dealing with the diagnostic, scenario, and sensitivity analysis. Considering the increasing incidence of soil erosion, the BBNs model proposed in this study can be extended to a variety of ecosystems that are subject to soil erosion and changes in the probability of its causal factors.</p></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"4 1","pages":"Pages 134-147"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666592123000914/pdfft?md5=545bd97fb75284ad20b2a84225f2164b&pid=1-s2.0-S2666592123000914-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135389743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative study on landslide susceptibility mapping based on different ratios of training samples and testing samples by using RF and FR-RF models","authors":"Ke Xu ,&nbsp;Zhou Zhao ,&nbsp;Wei Chen ,&nbsp;Jianquan Ma ,&nbsp;Fei Liu ,&nbsp;Yihao Zhang ,&nbsp;Zijun Ren","doi":"10.1016/j.nhres.2023.07.004","DOIUrl":"10.1016/j.nhres.2023.07.004","url":null,"abstract":"<div><p>Evaluation of landslide susceptibility is essential to planning of land and space utilization. For this purpose, the paper presents a case study from Fugu County, Shaanxi Province, China. Firstly, the geological environment and current state of landslides in Fugu County were investigated. Then, slope, aspect, terrain relief, curvature, lithology, land type, and normalized difference vegetation index (NDVI) were considered as the landslide susceptibility condition factors, and the correlation between these carried out by using Multicollinearity Analysis method. Next, landslide and non-landslide samples were divided into training samples and testing samples according to the sample <em>ratios</em> of 8/2, 7/3, 6/4, and 5/5, respectively. The landslide susceptibility mapping was carried out by using Random Forest (RF) model and Frequency Ratio coupled with Random Forest (FR-RF) model, respectively. Lastly, the landslide density (LD), landslide frequency ratio (LFR), the area under the curve (AUC) of the receiver operator, and other indicators were used to validate the rationality, accuracy, and performance of the landslide susceptibility maps produced from different models and <em>ratios</em>. The results indicated that all maps are reasonable, except the map when <em>ratio</em> is 5/5. For each map, regardless of <em>ratios</em>, the LD and LFR are the greatest in the zones classed as having a very high susceptibility, followed by those with a high, moderate, low, and very low classes.</p><p>In the Random Forest (RF) model, when the training test set is not at the same time its in the area of extremely high sensitivity of LD and the size of the FR value respectively 7/3 (201.026) ​&gt; ​8/2 (154.440) ​&gt; ​6/4 (93.696) &gt;5/5 (136.364) and 7/3 (4.806) ​&gt; ​8/2 (3.692) ​&gt; ​6/4 (3.260) ​&gt; ​5/5 (2.240); in the Frequency Ratio coupled with Random Forest (FR-RF) model, Inall the training test sets the size of the proportion of LD and FR value respectively 7/3 (145.693) ​&gt; ​6/4 (127.151) ​&gt; ​5/5 (122.857) ​&gt; ​8/2 (113.263) and 7/3 (3.334) ​&gt; ​6/4 (3.073) ​&gt; ​5/5 (2.811) ​&gt; ​8/2 (2.592). What else, from the comparison of ROC curves, when <em>ratio</em> is 7/3, the accuracy of the two models is higher than that of other <em>ratios</em>. Similarly, the results of the ensemble model (A combination of two models with different learning abilities.) are not more reasonable than the results of the single model, which reflects that the combination of a weaker learner model (Frequency Ratio model here) with a stronger learner model (Random Forest model here) can diminish the performance of the stronger model.</p></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"4 1","pages":"Pages 62-74"},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666592123000732/pdfft?md5=57f6bcca382435f449d5967b78339074&pid=1-s2.0-S2666592123000732-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90178144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}