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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

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

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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

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Modeling women's perceived level of safety at night in Milan

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Journal of Transport Geography 2025年 127卷

作者： Messa, Federico Scarponi, Lily Abdelfattah, Lamia Carpentieri, Gerardo Martinelli, Valerio Stiuso, Tonia Gorrini, Andrea Fondazione Transform Transport ETS Milan Italy TeMA Lab Dipartimento di Ingegneria Civile Edile e Ambientale Università degli Studi di Napoli Federico II Naples Italy

This article outlines the data analysis and modeling processes of the perceived level of safety for women at night time in the city of Milan. This research effort consists of the final phase of the STEP UP project, which was aimed to assess walkability for women in Milan using geospatial data analysis methods and involved three phases: literature review, data collection, and data analysis. The modeling of perceived safety proposed in this paper takes into consideration a wide set of geospatial data, acquired from open data and proprietary platforms, quantifying factors identified in the literature review, and the Wher app data, a crowdsourced dataset of women's self-disclosed perceived safety on the street network. These two sets of information are used to explore the relationship between the built environment and safety perception. Moreover, this article covers the processes of filtering the factors emerged from scientific literature, and the translation from concepts into operationalizable quantitative indicators. The process of identifying the relations between the safety factors and the perceived safety consisted in testing several statistical models, ultimately leading to the implementation of a Geographically Weighted Regression (GWR) model, which appeared as the most suitable for capturing the spatial heterogeneity of the factors. The results of the project showed the presence of three statistically significant indicators, namely Public Lighting, Public Transport, and Food & Beverage, and include the spatial variation of Local R2 and Beta coefficient values, allowing for the inference of site-specific phenomena and complexities. Overall, the approach proposed in this article aims to bridge the gap between subjective crowdsourced data and city characteristics data, proposing a layered methodology to fine-tune the association between factors relevant to safety and geospatial data. It also proposed a quantitative approach to model the nighttime safety perception

关键词： Gendered walkability Geographically weighted regression model geospatial data analysis Safety perception in public spaces

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How much district heating in a future sector-coupled energy system? A comprehensive methodology for mapping and clustering the infrastructure costs on an entire territory

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Energy 2025年 330卷

作者： L.M. Hofmann A. Clerjon F. Perdu R. Bavière P. Azaïs CEA DES I-Tésé Université Paris Saclay Gif sur Yvette France Univ. Grenoble Alpes IMEP2 110 rue de la Chimie 38400 Saint-Martin-d'Hères France Univ. Grenoble Alpes CEA LITEN F-38000 Grenoble France DistrictLab 26 rue Gustave Eiffel 38000 Grenoble France Univ. Grenoble Alpes CEA DES DPE 17 rue des Martyrs 38054 Grenoble France

In the energy transition context, district heating have great potential for generating low-carbon and cost-effective heat. They can also provide flexibility to the electrical system thanks to sector coupling solutions and thermal energy storage allowing, for instance, better integration of intermittent renewable energy sources. Therefore, assessing the competitiveness of district heating would require a sector-coupled optimisation model with, as an input parameter, the costs of the district heating infrastructure estimated throughout the entire territory (a geographic zone of a given size) at a high spatial resolution, gathered in ready-to-implement clusters. Building on the work from Persson and Werner, we present a methodology to map and cluster district heating network costs on an entire territory. It relies on easy-to-access geospatial key variables such as heat demand, building density and winter temperatures. As a case study, we apply it to France and design two clustering approaches to gather the infrastructure costs: competitiveness-based and territory typology-based – to satisfy different model architectures. We observe, for instance, a 51 TWh annual heat supply potential for district heating in French urban areas for a total investment of 10 b€. With this comprehensive and replicable methodology, we aim to pave the way for a new and more accurate competitiveness assessment of district heating in sector-coupled models.

关键词： District heating District heating network Clustering Investment cost assessment geospatial data analysis Heat-and-electricity coupling Energy system modelling

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

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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

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Answering Spatial Density Queries Under Local Differential Privacy

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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

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Movement of trichloroethene in a discontinuous permafrost zone

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JOURNAL OF CONTAMINANT HYDROLOGY 2011年第1-4期124卷 1-13页

作者： Carlson, Andrea E. Barnes, David L. Univ Alaska Fairbanks Civil & Environm Engn Water & Environm Res Ctr Fairbanks AK 99775 USA Shannon & Wilson Inc Fairbanks AK USA

At a site with discontinuous permafrost in Fairbanks, Alaska, releases of trichloroethene (TCE), an industrial solvent, have caused contamination of the groundwater. The objective of this study was to investigate the relationship between the migration pathway of the TCE groundwater plume and the distribution of the discontinuous permafrost at the site. The ICE plume configuration is substantially different than what regional hydrology trends would predict. Using GIS, we conducted a geostatistical analysis of field data collected during soil-boring installations and groundwater monitoring well sampling. With the analysis results, we constructed maps of the permafrost-table elevation (top of permafrost) and of the groundwater gradients and ICE concentrations from multiyear groundwater sampling events. The plume concentrations and groundwater gradients were overlain on the permafrost map to correlate permafrost locations with groundwater movement and the spatial distribution of ICE moving with groundwater. Correlation of the overlay maps revealed converging and diverging groundwater flow in response to the permafrost-table distribution, the absence of groundwater contamination in areas with a high permafrost-table elevation, and channeling of contaminants and water between areas of permafrost. In addition, we measured groundwater elevations in nested wells to quantify vertical gradients affecting ICE migration. At one set of nested wells down gradient from an area of permafrost we measured an upward vertical gradient indicating recharge of groundwater from the subpermafrost region of the aquifer causing dilution of the plume. The study indicates that the variable distribution of the permafrost is affecting the way groundwater and ICE move through the aquifer. Consequently, changes to the permafrost configuration due to thawing would likely affect both groundwater movement and TCE migration, and areas that were contaminant-free may become susceptible to contamination. (

关键词： Discontinuous permafrost Trichloroethene Vertical groundwater gradients geospatial data analysis

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Tuberculosis Prevention Model in Developing Countries based on geospatial, Cloud and Web Technologies

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2020年第1期11卷 219-232页

作者： Mwila, Innocent Phiri, Jackson Univ Zambia Dept Elect & Elect Engn Lusaka Zambia Univ Zambia Dept Comp Sci Lusaka Zambia

Information is important when making decisions. Decisions which are based on gut feeling and made in the absence of evidence always tend to be less effective in most situations. This is also the case when it comes to Tuberculosis (TB) disease control and prevention intervention planning and implementation. The lack of evidence-based information upon which decisions for action to help with the prevention of spread of TB has proved to be less effective in the prevention of the disease as TB keeps spreading. The aim of this paper was to design and develop a prototype system that would provide TB program managers with information and tools which can be used to make decisions which can effectively influence the fight against the spread of TB through the application of cloud computing, geospatial data analysis and web technologies. The system would improve disease monitoring and tracking through the use of the identified technologies, by displaying the geographical distribution of TB cases in the communities on a mapping application as well as providing reports which TB program managers can use to make decisions when planning and implementing disease control and prevention activities.

关键词： Evidence-based monitoring cloud computing geospatial data analysis mapping web technologies information decision-making tuberculosis prevention

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A general theory of spatial relations to support a graphical tool for visual information extraction

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JOURNAL OF VISUAL LANGUAGES AND COMPUTING 2013年第2期24卷 71-87页

作者： Della Penna, Giuseppe Magazzeni, Daniele Orefice, Sergio Univ Aquila Dept Comp Sci I-67100 Laquila Italy Kings Coll London Dept Informat London WC2R 2LS England

In this paper we present a general spatial composition framework which allows one to model the graphical objects and the spatial relations of a large class of visual languages. The new formalism has been implemented within the SRQ tool, a software system for the Visual Information Extraction, enabling it to work on a wider range of domains. In particular, in the paper we describe the application of SRQ to geospatial data. (c) 2012 Elsevier Ltd. All rights reserved.

关键词： Information extraction Visual information search Spatial relations geospatial data analysis

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

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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

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