Natural Hazards Research最新文献

{"title":"Flood prediction with time series data mining: Systematic review","authors":"Dimara Kusuma Hakim ,&nbsp;Rahmat Gernowo ,&nbsp;Anang Widhi Nirwansyah","doi":"10.1016/j.nhres.2023.10.001","DOIUrl":"10.1016/j.nhres.2023.10.001","url":null,"abstract":"<div><p>The global community is continuously working to minimize the impact of disasters through various actions, including earth surveying. For example, flood-prone areas must be identified appropriately, predicted, understood, and socialized. In that case, it will increase the risk of disaster impacts on the affected population in the form of death, property damage, and socio-economic losses.</p><p>The data mining approach has had a significant influence on research related to flood prediction in recent years, namely its impact on researchers related to forecast, classification, and clustering. Floods can also be predicted using a time series approach used to predict the future, a type of data-driven prediction that has been developed and widely applied and can be applied to predictions related to hydrology.</p><p>A review to identify, evaluate, and interpret all relevant research results carried out so far for flood prediction and flood prediction with a data mining approach. The review method used in this study is PRISMA as a tool and guide for evaluating systematic reviews and meta-analyses.</p><p>Some things discussed are types of data, types of floods and their parameters, types of approaches and combinations, and evaluation methods used in related studies. This study found that although the univariate time series approach dominates in related studies, multivariate time series Analysis (53 papers or 48.62%) can also be used to strengthen flood predictions in the long term or short term, t; this is an opportunity for further research. Some research opportunities to be carried out are combining the team series approach and the Estimation or Classification approaches. In contrast, the optimization approach is 11% of the total study. This is the next research opportunity. The type of flood chosen is also an opportunity for research to find a research gap; the less response on a kind of flood, the easier the study will be. This review found four types of floods: River Flood (76.1%), Urban Flood (11.9%), Coastal Flood (6.4%), and Flash Flood (5.5%). The dominant use of the evaluation method is RMSE, although this method is an absolute measure on the same scale as the target (depending on the data). Methods that produce percentages, such as MAPE, which are easier to understand by end users, need to be used more frequently in future studies. The amount of data also determines whether the resulting model is good, especially the choice of the time series approach, whether long-term or short-term. Whether short-term or long-term, forecasting is essential in disaster mitigation, which in this study is related to floods based on time series. Short-term forecasting can be used as an early warning system, while long-term forecasting can be used to support infrastructure planning by the government.</p></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"4 2","pages":"Pages 194-220"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666592123000963/pdfft?md5=a2d793a0e41cfbc61d4b6676e8c22050&pid=1-s2.0-S2666592123000963-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135705996","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 estimation and risk prediction using multispectral satellite images: Case study","authors":"Nazimur Rahman Talukdar ,&nbsp;Firoz Ahmad ,&nbsp;Laxmi Goparaju ,&nbsp;Parthankar Choudhury ,&nbsp;Rakesh Arya ,&nbsp;Abdul Qayum ,&nbsp;Javed Rizvi","doi":"10.1016/j.nhres.2024.01.007","DOIUrl":"10.1016/j.nhres.2024.01.007","url":null,"abstract":"<div><h3>Introduction</h3><p>Forest fires are increasing in terms of number, size, and extent which have a growing influence on the achievement of the Sustainable Development Goals (SDGs). The economy and ecology of Northeast India have been seriously impacted by forest fires in many places, it is important to comprehend the region's spatiotemporal distribution, severity, and future projections for forest fires in light of climate change.</p></div><div><h3>Methods</h3><p>Geographical information systems (GIS) integrating with remote sensing (RS) were used to understand the role of different parameters in all four bioclimatic zones of the region.</p></div><div><h3>Results</h3><p>and discussion: Most of the fires were restricted to pre-monsoon season (93 %), alone 62 % in March. The forest fire in the present scenario was highest in the Lawngtlai district, followed by Dhalai and Ri-Bhoi. The Lawngtlai and Dhalai districts are at the highest risk (greater than 70 %) for future forest fires. Categorically, among the protected areas, Lengteng WLS has the highest (86.6 %) future forest fire risk followed by Tawi WLS (86.5 %), Ngengpui WLS (84.9 %), and Pualreng WLS (84.6 %).</p></div><div><h3>Conclusion</h3><p>The results suggest that underground biomass in the lower elevated forest needs to be managed effectively at the onset of the fire season to reduce the occurrence of forest fires. There is a need for a well-defined framework supported by geospatial technology to predict, identify, and prioritize the fire potential zone with synergic strategies supported by the local community to mitigate the fire impact on the forests.</p></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"4 2","pages":"Pages 304-319"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666592124000167/pdfft?md5=5a0e0af0fce4fe7246d09a1af16cdc37&pid=1-s2.0-S2666592124000167-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139632538","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":"Assessment of shoreline changes and associated erosion and accretion pattern in coastal watersheds of Tamil Nadu, India","authors":"Leo George Sekar ,&nbsp;X. Androws ,&nbsp;K. Annaidasan ,&nbsp;Ashish Kumar ,&nbsp;R. Kannan ,&nbsp;G. Muthusankar ,&nbsp;K. Balasubramani","doi":"10.1016/j.nhres.2023.09.008","DOIUrl":"10.1016/j.nhres.2023.09.008","url":null,"abstract":"<div><p>The coastal region is not a constant and depends on several physical elements. Analogous to the world trend, the coastal areas of Tamil Nadu are encountering sea level rise and associated shoreline changes. However, the rate of change is not uniform and varies considerably. The study revealed such differences in shoreline changes during the past 30 years and identified erosion and accretion patterns trends in Tamil Nadu's coastal watersheds, from Pulicat (Thiruvallur district) to Kodyar (Kanyakumari district). The study used remotely sensed Landsat TM (Thematic Mapper) and OLI (Operational Land Imager) datasets to analyse shoreline changes from 1988 to 2018. The Digital Shoreline Analysis System (DSAS) tool is used to derive trends of shoreline changes by End Point Rate (EPR) and Net Shoreline Movement (NSM). The mean EPR is- 0.26 ​m/yr and NSM is-8.03 ​m/yr, suggesting the overall shoreline of the Tamil Nadu coast is shifting landward. However, the annual rates of EPR and NSM vary considerably from watershed to watershed, and therefore, these indices were used to categorise the coastal watersheds based on erosions and accretions. The results identified a high erosion rate in the watersheds of Coleroon, Arasalar (Nagapattinam), Gundar (Ramanathapuram), Vembar, Lower Vaippar (Thoothukkudi), Nambiyar, Hanuma (Tirunelveli), and Putian, Kodyar (Kanyakumari). Field surveys were conducted to verify ground conditions at 139 random locations along the 1000 ​km shoreline stretch. About 75% of respondents said they experience a very high to high risk of coastal erosion. The rates estimated by the study and categorisation of the coastal watersheds could be most helpful in evaluating the cumulative impact of coastal hazards and preparing sustainable development plans. The outcomes may also help to create awareness in more susceptible areas.</p></div>","PeriodicalId":100943,"journal":{"name":"Natural Hazards Research","volume":"4 2","pages":"Pages 231-238"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666592123000902/pdfft?md5=8a9ec76307cafce38bfb86175d2820f0&pid=1-s2.0-S2666592123000902-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135389608","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":"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":"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":"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":"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}