检索结果-内蒙古大学图书馆

Physics-aware wind surrogate model for UAV aerodynamic response assessment

INTERNATIONAL JOURNAL OF NUMERICAL METHODS FOR HEAT & FLUID FLOW 2025年

作者： Veiga-Pineiro, Gonzalo Dominguez, Pablo Aldao, Enrique Fontenla-Carrera, Gabriel Veiga-Lopez, Fernando Martin, Elena B. Gonzalez-Jorge, Higinio Univ Vigo Inst Phys & Aerosp Sci IFCAE Sch Aeronaut & Space Orense Spain

PurposeThis study aims to use dimensionality reduction techniques applied to a detailed wind flow computational fluid dynamics (CFD)-generated database to develop a fast numerical tool that predicts, using the available weather forecast data, the airflow around any urban environment. The tool is aimed for its use in path planning design and optimization of unmanned aerial vehicles (UAVs) in urban ***/methodology/approachA complex urban site is selected as an example of vertiport. geospatial data and land models are used to automate the CFD computational domain, mesh generation and terrain classification. To enhance efficiency, some mesh cells, corresponding to dense vegetation and remote buildings, are solved as porous media. After validation, a CFD database is created using a Reynolds-averaged Navier-Stokes model by sweeping different wind flow boundary conditions. The database is processed with high order singular value decomposition techniques, and interpolation methods enable real-time wind flow predictions, producing detailed maps with resolution under 1 m in approximately 1 *** surrogate model accelerates predictions by a factor of 7200 compared to direct CFD simulations while maintaining acceptable accuracy: mean relative deviations in velocity predictions near the buildings of interest are of the order of 2%. Examples of UAV trajectories and their dynamic responses are obtained using the developed ***/valueThe computational domain is automated using geospatial data, facilitating mesh classification and improving simulation efficiency. The surrogate model, which uses wind forecasts from the meteorological as inputs, provides real-time wind-flow predictions and improves UAV flight path design by identifying high-risk areas before take-off.

关键词： Fast prediction model Reduced order model (ROM) Surrogate model High order singular value decomposition (HOSVD) Computational fluid dynamics (CFD) geospatial data analysis

来源：评论

学校读者我要写书评

暂无评论

Answering Spatial Density Queries Under Local Differential Privacy

引用

IEEE INTERNET OF THINGS JOURNAL 2024年第10期11卷 17419-17436页

作者： Tire, Ekin Gursoy, M. Emre Koc Univ Dept Comp Engn TR-34450 Istanbul Turkiye

Spatial density queries are fundamental in many geospatial data analysis and crowdsourcing tasks. However, answering spatial density queries may violate users' privacy by exposing their locations to an untrusted data collector. In this article, we propose a solution for answering spatial density queries under local differential privacy (LDP), a state-of-the-art privacy protection standard. Our solution consists of four main steps: 1) partitioning;2) finding sensitivity;3) user-side noisy response computation;and 4) server-side estimation. For the first step, we propose and analyze three basic partitioning strategies, and based on our analysis, we design an improved strategy called Advanced Partitioning. For the second step, we adapt graph-based modeling of query sets from the centralized differential privacy literature. Advanced Partitioning also leverages and extends this technique by formulating the partitioning problem using vertex coloring. For the third and fourth steps, in addition to adapting two popular LDP protocols (GRR and RAPPOR), we propose a novel extension for the optimized unary encoding protocol. Our new protocol (OBE) is not only applicable to our problem but can also be used in other LDP problems with bitvector encodings. Finally, we perform an extensive experimental evaluation of different partitioning strategies and protocols using multiple real-world data sets. Results show that Advanced Partitioning and OBE yield the lowest error, demonstrating the superiority of our proposed methods.

关键词： Protocols Differential privacy Sensitivity Privacy Servers Estimation Spatial databases data privacy geospatial data analysis local differential privacy (LDP) location privacy spatial crowdsourcing

来源：评论

学校读者我要写书评

暂无评论

Beyond the richter scale: a fuzzy inference system approach for measuring objective earthquake risk

引用

NATURAL HAZARDS 2025年第1期121卷 245-268页

作者： Mohammadi, Shahin Balouei, Fatemeh Amini, Saeid Rabiei-Dastjerdi, Hamidreza Shahid Chamran Univ Ahvaz Dept Remote Sensing Ahvaz Fateme Iran Shahid Chamran Univ Ahvaz Fac Earth Sci GIS Ahvaz Fateme Iran Univ Isfahan Fac Civil Engn Dept Geomatics Engn Esfahan Iran Univ Coll Dublin UCD Sch Architecture Planning & Environm Policy Dublin Ireland Univ Coll Dublin UCD CeADAR Irelands Natl Ctr Appl Data Analyt & AI Dublin Ireland Isfahan Univ Med Sci Social Determinants Hlth Res Ctr Esfahan Iran

Introducing a novel Fuzzy Inference System (FIS) approach that integrates satellite and GIS data for precise earthquake risk mapping and minimizing *** comprehensive earthquake risk assessment through the integration of diverse data *** the credibility of modeling results by validating with historical earthquake data, providing valuable insights for policymakers addressing natural hazards. Earthquakes pose significant natural hazards and impact populations worldwide. Iran is among the most susceptible countries to seismic activity, making comprehensive earthquake risk assessment crucial. This study employs geospatial methods, including integrating satellite, ground-based, and auxiliary data to model earthquake risk across this country. A Fuzzy Inference System (FIS) is used to generate earthquake hazard probability and vulnerability layers, considering factors such as slope, elevation, fault density, building density, proximity to main roads, proximity to buildings, population density, and earthquake epicenter, magnitude, proximity to the epicenter, depth density, peak ground acceleration (PGA). The results highlight high-risk areas in the Alborz and Zagros Mountain ranges and coastal regions. Moreover, the findings indicate that 39.7% (approximately 31.7 million people) of Iran's population resides in high-risk zones, with 9.6% (approximately 7.7 million) located in coastal areas vulnerable to earthquakes. These findings offer valuable insights for crisis management and urban planning initiatives.

关键词： Natural hazards Earthquake risk assessment geospatial data analysis Fuzzy inference system (FIS) geospatial methods Seismic activity Iran

来源：评论

学校读者我要写书评

暂无评论

Exploring the potential of social media to study environmental topics and natural disasters

引用

JOURNAL OF LOCATION BASED SERVICES 2023年第4期17卷 355-369页

作者： Milleville, Kenzo Van Ackere, Samuel Verdoodt, Jana Verstockt, Steven De Maeyer, Philippe van de Weghe, Nico Univ Ghent CartoGis Ghent Belgium Univ Ghent IDLab Imec Ghent Belgium

Social media has become an important means of communication and new insights can be gained from processing this data on a large scale. Our goal is to develop and implement a pipeline to automatically extract and analyse Twitter data on natural disasters and environmental topics. We aim to provide an additional layer of spatiotemporal data that can be used to study the immediate and lasting impacts of natural disasters, climate change, and environmental topics on the global population. An initial analysis of forest fires was conducted in four different languages confirming the need for multilingual support for global analysis. We found a positive correlation between wildfire occurrence and tweeting behaviour, as well as the geographic spread of fires. We found that simple sentiment predictions add little value when aggregating data on a large scale. A subsequent test using a fine-tuned stance detection model proved promising in determining the stance of tweets towards nuclear energy. We intend to expand our dataset and develop customised models in the future that can be used to analyse the global impact of natural disasters and environmental topics.

关键词： Social media geospatial data analysis Natural language processing Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Generalizing OD Maps to Explore Multi-dimensional geospatial datasets

引用

CARTOGRAPHIC JOURNAL 2024年第1期61卷 49-68页

作者： Liu, Liqun Vuillemot, Romain Riviere, Philippe Boy, Jeremy Tabard, Aurelien Ecole Cent Lyon LIRIS SICAL Lyon France VisionsCarto Lyon France UNDP Accelerator Labs New York NY USA Univ Lyon LIRIS INSA Lyon UCBLCNRS Lyon France

Understanding the mobility of entities in geospatial data is important to many fields, ranging from the social sciences to epidemiology, economics or air traffic control. Visualizing such entities can be challenging as it requires preserving both their explicit properties (spatial trajectories) and their implicit properties (abstract attributes of those trajectories). An existing technique called origin-destination maps preserves both explicit and implicit properties of datasets, using the spatial nesting technique. In this paper, we aim at generalizing this technique beyond an origins-and-destinations dataset (2-attribute datasets), to explore multi-dimensional datasets (N-attribute datasets) with the nesting approach. We present an abstraction framework - we call Gridify - and an interactive open-source tool implementing this framework using several levels of nested maps. We report on several case studies representative of the types of dimensions found in geospatial datasets (quantitative, temporal, discrete, boolean), showing the applicability of this approach to achieve visual exploratory analysis tasks in various application domains.

关键词： Exploratory data analysis geospatial data analysis grids

来源：评论

学校读者我要写书评

暂无评论

ForeINTiFlood: A Novel Framework for Forensic Investigation of Coastal Tidal Floods in The Pekalongan Coastal Area, Central Java, Indonesia

引用

EARTH SYSTEMS AND ENVIRONMENT 2024年 1-36页

作者： Yulianto, Fajar Wibowo, Mardi Yananto, Ardila Perdana, Dhedy Husada Fadjar Prabowo, Yudhi Wiguna, Edwin Adi Khoirunnisa, Hanah Aziz, Hilmi Nurwijayanti, Amalia Fachrudin, Imam Kongko, Widjo Natl Res & Innovat Agcy BRIN Res Ctr Hydrodynam Technol Surabaya 60112 East Java Indonesia

This study proposes a novel method, the Forensic Investigations Tidal Flood (ForeINTiFlood) framework, to investigate tidal floods in the Pekalongan coast, a region highly susceptible to these events. The key challenge lies in identifying the primary drivers of such floods. ForeINTiFlood addresses this gap by employing a multi-source-temporal geospatial data framework that integrates machine learning and hydrodynamic modeling. This framework offers a robust suite of tools for forensic investigations of coastal tidal flooding. The method leverages a comprehensive set of geo-bio-physical parameters encompassing topographic, bathymetric, shoreline change, land use and land cover, human infrastructure, ecological, and coastal hydrodynamic data. The effectiveness of ForeINTiFlood is assessed through the evaluation of variable importance in machine learning models trained on data spanning 2000 to 2020. The analysis yielded topography, ecology, land use and land cover, and human infrastructure as the most influential variables, with average importance scores of 0.387, 0.245, 0.199, and 0.160, respectively. Accuracy assessments using the k-fold approach demonstrated high performance for Random Forest (RF), Gradient Tree Boosting (GTB), and Classification and Regression Tree (CART) models, achieving average accuracies of 0.927, 0.924, and 0.908, respectively. Furthermore, a positive correlation was observed between the predicted flood probability from the machine learning models and the actual floodwater depth obtained from hydrodynamic modeling for the periods 2000, 2005, 2010, 2015, and 2020, with correlation values ranging from 0.52 to 0.72. These findings suggest a strong link between the model's predictions and real-world flood occurrences. ForeINTiFlood forensic investigation identified three specific areas (central, western, and eastern) as potential entry points for tidal floods. High-resolution satellite imagery further corroborated these findings. These results hol

关键词： Central Java (Indonesia) Coastal flood investigation ForeINTiFlood framework geospatial data analysis Hydrodynamic modeling Machine learning techniques Multi-source-temporal data Pekalongan coastal area Tidal flood analysis

来源：评论

学校读者我要写书评

暂无评论

Assessment of geographical accessibility to COVID-19 testing facilities in Nepal (2021)

引用

LANCET REGIONAL HEALTH - SOUTHEAST ASIA 2024年 27卷 100436-100436页

作者： Krishnakumari, Parvathy Krishnan Bakker, Hannah Lahrichi, Nadia Cote, Fannie L. Gromicho, Joaquim Govindakarnavar, Arunkumar Jha, Priya Shrestha, Saugat Mulmi, Rashmi Bhusal, Nirajan Stapith, Deepesh Jha, Runa Shrestha, Lilee Samuel, Reuben Naidoo, Dhamari Vilas, Victor Del Rio Univ Amsterdam Amsterdam Business Sch Amsterdam Netherlands Karlsruhe Inst Technol Karlsruhe Germany Polytech Montreal Dept Math & Ind Engn Montreal PQ Canada ORTEC Zoetermeer Netherlands WHO Country Off Nepal Kathmandu Nepal Natl Publ Hlth Lab Kathmandu Nepal WHO South East Asia Reg Off New Delhi India UK Hlth Secur Agcy Publ Hlth Rapid Support Unit London England

Background Ensuring equitable physical access to SARS-CoV-2 testing has proven to be crucial for controlling the COVID-19 epidemic, especially in countries like Nepal with its challenging terrain. During the second wave of the pandemic in May 2021, there was immense pressure to expand the laboratory network in Nepal to ensure calibration of epidemic response. The expansion led to an increase in the number of testing facilities from 69 laboratories in May 2021 to 89 laboratories by November 2021. We assessed the equity of physical access to COVID-19 testing facilities in Nepal during 2021. Furthermore, we investigated the potential of mathematical optimisation in improving accessibility to COVID-19 testing facilities. Methods Based on up-to-date publicly available data sets and on the COVID-19-related daily reports published by Nepal ' s Ministry of Health and Population from May 1 to November 15, 2021, we measured the disparities in geographical accessibility to COVID-19 testing across Nepal at a resolution of 1 km 2 . In addition, we proposed an optimisation model to prescribe the best possible locations to set up testing laboratories maximizing access, and tested its potential impact in Nepal. Findings The analysis identi fi ed vulnerable districts where, despite ramping up efforts, physical accessibility to testing facilities remains low under two modes of travel - walking and motorized driving. Both geographical accessibility and its equality were better under the motorised mode compared with the walking mode. If motorised transportation were available to everyone, the population coverage within 60 min of any testing facility (public and private) would be close to threefold the coverage for pedestrians within the same hour: 61.4% motorised against 22.2% pedestrian access within the hour, considering the whole population of Nepal. Very low accessibility was found in most areas except those with private test centres concentrated in the capital city of Kathmandu. T

关键词： geospatial data analysis Access to COVID-19 diagnostic laboratories Optimised location of health facilities

来源：评论

学校读者我要写书评

暂无评论

Analyzing Tweeting Patterns and Public Engagement on Twitter During the Recognition Period of the COVID-19 Pandemic: A Study of Two US States

引用

IEEE ACCESS 2022年 10卷 72879-72894页

作者： Ul Hoque, Misbah Lee, Kisung Beyer, Jessica L. Curran, Sara R. Gonser, Katie S. Lam, Nina S. N. Mihunov, Volodymyr V. Wang, Kejin Louisiana State Univ Div Comp Sci & Engn Baton Rouge LA 70803 USA Univ Washington Henry M Jackson Sch Int Studies Seattle WA 98195 USA Louisiana State Univ Dept Environm Sci Baton Rouge LA 70803 USA

The abundance of available information on social media can provide invaluable insights into people's responses to health information and public health guidance concerning COVID-19. This study examines tweeting patterns and public engagement on Twitter, as forms of social media, related to public health messaging in two U.S. states (Washington and Louisiana) during the early stage of the pandemic. We analyze more than 7M tweets and 571K COVID-19-related tweets posted by users in the two states over the first 25 days of the pandemic in the U.S. (Feb. 23, 2020, to Mar. 18, 2020). We also qualitatively code and examine 460 tweets posted by selected governmental official accounts during the same period for public engagement analysis. We use various methods for analyzing the data, including statistical analysis, sentiment analysis, and word usage metrics, to find inter- and intra-state disparities of tweeting patterns and public engagement with health messaging. Our findings reveal that users in Washington were more active on Twitter than users in Louisiana in terms of the total number and density of COVID-19-related tweets during the early stage of the pandemic. Our correlation analysis results for counties or parishes show that the Twitter activities (tweet density, COVID-19 tweet density, and user density) were positively correlated with population density in both states at the 0.01 level of significance. Our sentiment analysis results demonstrate that the average daily sentiment scores of all and COVID-19-related tweets in Washington were consistently higher than those in Louisiana during this period. While the daily average sentiment scores of COVID-19-related tweets were in the negative range, the scores of all tweets were in the positive range in both states. Lastly, our analysis of governmental Twitter accounts found that these accounts' messages were most commonly meant to spread information about the pandemic, but that users were most likely to engage with tweet

关键词： Social networking (online) COVID-19 Pandemics Blogs Public healthcare Media Sentiment analysis COVID-19 geospatial data analysis natural language processing public engagement public health messaging sentiment analysis statistical analysis Twitter data analytics

来源：评论

学校读者我要写书评

暂无评论

Smart hotspot detection using geospatial artificial intelligence: A machine learning approach to reduce flood risk

引用

SUSTAINABLE CITIES AND SOCIETY 2024年 115卷

作者： Rezvani, Seyed M. H. S. Goncalves, Alexandre Silva, Maria Joao Falcao de Almeida, Nuno Marques Univ Lisbon Civil Engn Res & Innovat Sustainabil CERIS Inst Super Tecn Ave Rovisco Pais 1 P-1049001 Lisbon Portugal Lab Nacl Engn Civil Ave Brasil 101 P-1700075 Lisbon Portugal

This study employs geospatial Artificial Intelligence (GeoAI) and the Random Forest Machine Learning (ML) algorithm to enhance flood hazard assessments in Portugal. It utilizes NASA's LP DAAC (2023) Digital Elevation Model (DEM) and slope data from EPIC WEBGIS PORTUGAL data, offering detailed topographical insights for environmental planning. Additionally, it incorporates data on proximity to water bodies from the Portuguese Environment Agency and the European Environment Agency, and soil characteristics from EPIC WEBGIS PORTUGAL data, facilitating a thorough examination of flood risks. This approach prioritizes long-term land features over short-term weather patterns, providing a comprehensive understanding of flood vulnerability. The study processes data at a 1 km x 1 km resolution, adapting TIFF maps for compatibility with the Random Forest model. The produced flood hazard maps identify potential flood hotspots at both national and city levels, crucial for urban planning. These maps aid in assessing the vulnerability of key infrastructure and assets, such as transport networks and buildings. The research highlights the importance of integrating additional data on assets and socioeconomic factors to enhance urban resilience. It sets the stage for future research aimed at improving predictive accuracy and underscores the necessity of extensive geospatial analytics in managing infrastructure risks.

关键词： geospatial artificial intelligence Flood hazard mapping Machine learning Urban resilience Disaster risk reduction geospatial data analysis

来源：评论

学校读者我要写书评

暂无评论

Comparison of Various Deep CNN Models for Land Use and Land Cover Classification 13th

Comparison of Various Deep CNN Models for Land Use and Land ...

引用

13th International Conference on Intelligent Human Computer Interaction (IHCI)

作者： Mahamunkar, Geetanjali S. Netak, Laxman D. Dr Babasaheb Ambedkar Technol Univ Lonere Dept Comp Engn Raigad 402103 India

ISBN: (纸本)9783030984045;9783030984038

Activities of identifying kinds of physical objects on lands from the images captured through satellite and labeling them according to their usages are referred to as Land Use and Land Cover Classification (LULC). Researchers have developed various machine learning techniques for this purpose. The effectiveness of these techniques has been individually evaluated. However, their performance needs to be compared against each other primarily when they are used for LULC. This paper compares the performance of five commonly used machine learning techniques, namely Random Forest, two variants of Residual Networks, and two variants of Visual Geometry Group Models. The performance of these techniques is compared in terms of accuracy, recall and precision using the Eurosat dataset. The performance profiling described in this paper could help researchers to select a given model over other related techniques.

关键词： geospatial data analysis Deep learning Pre-trained deep CNN models Human centric deep learning API Land Use and Land Cover Classification

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：