1,561 research outputs found

    Flood dynamics derived from video remote sensing

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    Flooding is by far the most pervasive natural hazard, with the human impacts of floods expected to worsen in the coming decades due to climate change. Hydraulic models are a key tool for understanding flood dynamics and play a pivotal role in unravelling the processes that occur during a flood event, including inundation flow patterns and velocities. In the realm of river basin dynamics, video remote sensing is emerging as a transformative tool that can offer insights into flow dynamics and thus, together with other remotely sensed data, has the potential to be deployed to estimate discharge. Moreover, the integration of video remote sensing data with hydraulic models offers a pivotal opportunity to enhance the predictive capacity of these models. Hydraulic models are traditionally built with accurate terrain, flow and bathymetric data and are often calibrated and validated using observed data to obtain meaningful and actionable model predictions. Data for accurately calibrating and validating hydraulic models are not always available, leaving the assessment of the predictive capabilities of some models deployed in flood risk management in question. Recent advances in remote sensing have heralded the availability of vast video datasets of high resolution. The parallel evolution of computing capabilities, coupled with advancements in artificial intelligence are enabling the processing of data at unprecedented scales and complexities, allowing us to glean meaningful insights into datasets that can be integrated with hydraulic models. The aims of the research presented in this thesis were twofold. The first aim was to evaluate and explore the potential applications of video from air- and space-borne platforms to comprehensively calibrate and validate two-dimensional hydraulic models. The second aim was to estimate river discharge using satellite video combined with high resolution topographic data. In the first of three empirical chapters, non-intrusive image velocimetry techniques were employed to estimate river surface velocities in a rural catchment. For the first time, a 2D hydraulicvmodel was fully calibrated and validated using velocities derived from Unpiloted Aerial Vehicle (UAV) image velocimetry approaches. This highlighted the value of these data in mitigating the limitations associated with traditional data sources used in parameterizing two-dimensional hydraulic models. This finding inspired the subsequent chapter where river surface velocities, derived using Large Scale Particle Image Velocimetry (LSPIV), and flood extents, derived using deep neural network-based segmentation, were extracted from satellite video and used to rigorously assess the skill of a two-dimensional hydraulic model. Harnessing the ability of deep neural networks to learn complex features and deliver accurate and contextually informed flood segmentation, the potential value of satellite video for validating two dimensional hydraulic model simulations is exhibited. In the final empirical chapter, the convergence of satellite video imagery and high-resolution topographical data bridges the gap between visual observations and quantitative measurements by enabling the direct extraction of velocities from video imagery, which is used to estimate river discharge. Overall, this thesis demonstrates the significant potential of emerging video-based remote sensing datasets and offers approaches for integrating these data into hydraulic modelling and discharge estimation practice. The incorporation of LSPIV techniques into flood modelling workflows signifies a methodological progression, especially in areas lacking robust data collection infrastructure. Satellite video remote sensing heralds a major step forward in our ability to observe river dynamics in real time, with potentially significant implications in the domain of flood modelling science

    UMSL Bulletin 2023-2024

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    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    UMSL Bulletin 2022-2023

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    The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This ļ¬fth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different ļ¬elds of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modiļ¬ed Proportional Conļ¬‚ict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classiļ¬ers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identiļ¬cation of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classiļ¬cation. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classiļ¬cation, and hybrid techniques mixing deep learning with belief functions as well

    Synthetic Aperture Radar (SAR) Meets Deep Learning

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    This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

    Northeastern Illinois University, Academic Catalog 2023-2024

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    https://neiudc.neiu.edu/catalogs/1064/thumbnail.jp

    Security Analysis: A Critical Thinking Approach

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    Security Analysis: A Critical-Thinking Approach is for anyone desiring to learn techniques for generating the best answers to complex questions and best solutions to complex problems. It furnishes current and future analysts in national security, homeland security, law enforcement, and corporate security an alternative, comprehensive process for conducting both intelligence analysis and policy analysis. The target audience is upper-division undergraduate students and new graduate students, along with entry-level practitioner trainees. The book centers on a Security Analysis Critical-Thinking Framework that synthesizes critical-thinking and existing analytic techniques. Ample examples are provided to assist readers in comprehending the material. Newly created material includes techniques for analyzing beliefs and political cultures. The book also functions as an introduction to Foreign Policy and Security Studies.https://encompass.eku.edu/ekuopen/1005/thumbnail.jp

    Modeling and Simulation in Engineering

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    The Special Issue Modeling and Simulation in Engineering, belonging to the section Engineering Mathematics of the Journal Mathematics, publishes original research papers dealing with advanced simulation and modeling techniques. The present book, ā€œModeling and Simulation in Engineering I, 2022ā€, contains 14 papers accepted after peer review by recognized specialists in the field. The papers address different topics occurring in engineering, such as ferrofluid transport in magnetic fields, non-fractal signal analysis, fractional derivatives, applications of swarm algorithms and evolutionary algorithms (genetic algorithms), inverse methods for inverse problems, numerical analysis of heat and mass transfer, numerical solutions for fractional differential equations, Kriging modelling, theory of the modelling methodology, and artificial neural networks for fault diagnosis in electric circuits. It is hoped that the papers selected for this issue will attract a significant audience in the scientific community and will further stimulate research involving modelling and simulation in mathematical physics and in engineering

    2023-2024 Boise State University Undergraduate Catalog

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    This catalog is primarily for and directed at students. However, it serves many audiences, such as high school counselors, academic advisors, and the public. In this catalog you will find an overview of Boise State University and information on admission, registration, grades, tuition and fees, financial aid, housing, student services, and other important policies and procedures. However, most of this catalog is devoted to describing the various programs and courses offered at Boise State

    Mechanisms for the Influence from Ice Nucleus Aerosols on Clouds and their Indirect Effects: Cloud Modelling

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    The role of multiple groups of primary biological aerosol particles (PBAPs) as ice nucleating particles (INPs), and of ice formation processes such as time-dependent freezing of various INPs, and various secondary ice production(SIP) mechanisms in overall ice concentration has been evaluated in a range of cloud systems by simulating them numerically with the state-of-the-art ā€˜Aerosol-Cloudā€™ (AC) model in a 3D mesoscale domain. Also, the mechanismsof aerosol indirect effects (AIEs) arising from anthropogenic INPs, and the responses to these AIEs from time-dependent INP freezing and SIP processes are investigated in the simulated clouds. The cloud systems simulated with AC are: events of summertime deep convection observed over Oklahoma, USA during the Midlatitude Continental Convective Cloud Experiment (MC3E) in 2011 on 1) 11 May, and 2) 20 May, and wintertime 3) orographic clouds observed during the Atmospheric Radiation Measurement Cloud Aerosol Precipitation Experiment (ACAPEX) on 07 February 2015 over North California, and 4) supercooled layer clouds observed over Larkhill, UK, during the Aerosol Properties, Processes And Influences on the Earthā€™s climate (APPRAISE) campaign on 18 February 2009.AC uses the dynamical core of the Weather Research and Forecasting (WRF) model, modified Geophysical Fluid Dynamic Laboratory (GFDL) radiation scheme, and hybrid bin-bulk microphysics scheme. AC is validated adequately with the coincident aircraft, ground-based, and satellite observations for all four cases. AC forms secondary ice through the Hallett-Mossop (HM) process of rime-splintering, and fragmentation during ice-ice collisions, raindrop freezing, and sublimation of dendritic snow and graupel. A measure of SIP is defined using theterm ā€˜ice enhancementā€™ (IE) ratio which is the ratio between the number concentration of total ice particles and active INPs at cloud tops. For both cases in MC3E, overall, PBAPs have little effect (+1-6%) on the cloud-liquid (droplet mean sizes, number concentrations, and their water contents) properties, overall ice concentration, and on precipitation. AC predicts the activity of various INPs with an empirical parameterization (EP). The EP is modified to represent the time-dependent approach of INP freezing in light of our published laboratory observations. It is predicted that the time dependence of INP freezing is not the main cause for continuous ice nucleation and precipitation in all simulated cases. Rather, the main mechanism of precipitation formation is the combination of various SIP mechanisms (in convection) and recirculation-reactivation of dust particles (in APPRAISE layer cloud episode). Also, for all cases, the inclusion oftime dependence of INP freezing causes little increase (about 10-20%) in the total ice concentration and ice from all SIP.Regarding SIP, in young developing convective clouds of MC3E (11 May), with tops > āˆ’15oC, the initial explosive growth is from the fast HM process, creating IE ratios as high as 103. By contrast, in mature convective clouds (tops < āˆ’20oC), fragmentation in ice-ice collisions prevails, creating IE ratios of up to about 102-103. Regarding AIEs from INPs, increasing anthropogenic pollution is predicted to exert a net cooling in APPRAISE, and a strong net warmingin MC3E (11 May). Furthermore, these net AIEs are mainly from glaciated clouds. Overall, the contribution to the AIEs from ice formation processes, such as time-dependent INP freezing and SIP, shows a high sensitivity with respect to anthropogenic INPs (about 20-60% increase in net AIEs). Also, two new indirect effects associated with ice initiation mechanisms are proposed here. These are, 1) the ā€˜SIPā€™ indirect effect, and 2) the ā€˜time-dependent INPā€™ indirect effect. It is predicted that in APPRAISE and MC3E, both SIP and time-dependent INP indirect effects form less than 30%, and more than 50% of the net AIE, respectively
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