2,431 research outputs found
Flood dynamics derived from video remote sensing
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
Climate Change and Critical Agrarian Studies
Climate change is perhaps the greatest threat to humanity today and plays out as a cruel engine of myriad forms of injustice, violence and destruction. The effects of climate change from human-made emissions of greenhouse gases are devastating and accelerating; yet are uncertain and uneven both in terms of geography and socio-economic impacts. Emerging from the dynamics of capitalism since the industrial revolution — as well as industrialisation under state-led socialism — the consequences of climate change are especially profound for the countryside and its inhabitants. The book interrogates the narratives and strategies that frame climate change and examines the institutionalised responses in agrarian settings, highlighting what exclusions and inclusions result. It explores how different people — in relation to class and other co-constituted axes of social difference such as gender, race, ethnicity, age and occupation — are affected by climate change, as well as the climate adaptation and mitigation responses being implemented in rural areas. The book in turn explores how climate change – and the responses to it - affect processes of social differentiation, trajectories of accumulation and in turn agrarian politics. Finally, the book examines what strategies are required to confront climate change, and the underlying political-economic dynamics that cause it, reflecting on what this means for agrarian struggles across the world. The 26 chapters in this volume explore how the relationship between capitalism and climate change plays out in the rural world and, in particular, the way agrarian struggles connect with the huge challenge of climate change. Through a huge variety of case studies alongside more conceptual chapters, the book makes the often-missing connection between climate change and critical agrarian studies. The book argues that making the connection between climate and agrarian justice is crucial
Natural and Technological Hazards in Urban Areas
Natural hazard events and technological accidents are separate causes of environmental impacts. Natural hazards are physical phenomena active in geological times, whereas technological hazards result from actions or facilities created by humans. In our time, combined natural and man-made hazards have been induced. Overpopulation and urban development in areas prone to natural hazards increase the impact of natural disasters worldwide. Additionally, urban areas are frequently characterized by intense industrial activity and rapid, poorly planned growth that threatens the environment and degrades the quality of life. Therefore, proper urban planning is crucial to minimize fatalities and reduce the environmental and economic impacts that accompany both natural and technological hazardous events
Digital Innovations for a Circular Plastic Economy in Africa
Plastic pollution is one of the biggest challenges of the twenty-first century that requires innovative and varied solutions. Focusing on sub-Saharan Africa, this book brings together interdisciplinary, multi-sectoral and multi-stakeholder perspectives exploring challenges and opportunities for utilising digital innovations to manage and accelerate the transition to a circular plastic economy (CPE).
This book is organised into three sections bringing together discussion of environmental conditions, operational dimensions and country case studies of digital transformation towards the circular plastic economy. It explores the environment for digitisation in the circular economy, bringing together perspectives from practitioners in academia, innovation, policy, civil society and government agencies. The book also highlights specific country case studies in relation to the development and implementation of different innovative ideas to drive the circular plastic economy across the three sub-Saharan African regions. Finally, the book interrogates the policy dimensions and practitioner perspectives towards a digitally enabled circular plastic economy.
Written for a wide range of readers across academia, policy and practice, including researchers, students, small and medium enterprises (SMEs), digital entrepreneurs, non-governmental organisations (NGOs) and multilateral agencies, policymakers and public officials, this book offers unique insights into complex, multilayered issues relating to the production and management of plastic waste and highlights how digital innovations can drive the transition to the circular plastic economy in Africa.
The Open Access version of this book, available at https://www.taylorfrancis.com, has been made available under a Creative Commons Attribution-Non Commercial-No Derivatives (CC-BY-NC-ND) 4.0 license
EQUI-VOCAL: Synthesizing Queries for Compositional Video Events from Limited User Interactions [Technical Report]
We introduce EQUI-VOCAL: a new system that automatically synthesizes queries
over videos from limited user interactions. The user only provides a handful of
positive and negative examples of what they are looking for. EQUI-VOCAL
utilizes these initial examples and additional ones collected through active
learning to efficiently synthesize complex user queries. Our approach enables
users to find events without database expertise, with limited labeling effort,
and without declarative specifications or sketches. Core to EQUI-VOCAL's design
is the use of spatio-temporal scene graphs in its data model and query language
and a novel query synthesis approach that works on large and noisy video data.
Our system outperforms two baseline systems -- in terms of F1 score, synthesis
time, and robustness to noise -- and can flexibly synthesize complex queries
that the baselines do not support.Comment: This is an extended technical report for the following paper: "Enhao
Zhang, Maureen Daum, Dong He, Brandon Haynes, Ranjay Krishna, and Magdalena
Balazinska. EQUI-VOCAL: Synthesizing Queries for Compositional Video Events
from Limited User Interactions. PVLDB, 16(11): 2714-2727, 2023.
doi:10.14778/3611479.3611482
Automated identification and behaviour classification for modelling social dynamics in group-housed mice
Mice are often used in biology as exploratory models of human conditions, due to their similar genetics and physiology. Unfortunately, research on behaviour has traditionally been limited to studying individuals in isolated environments and over short periods of time. This can miss critical time-effects, and, since mice are social creatures, bias results.
This work addresses this gap in research by developing tools to analyse the individual behaviour of group-housed mice in the home-cage over several days and with minimal disruption. Using data provided by the Mary Lyon Centre at MRC Harwell we designed an end-to-end system that (a) tracks and identifies mice in a cage, (b) infers their behaviour, and subsequently (c) models the group dynamics as functions of individual activities. In support of the above, we also curated and made available a large dataset of mouse localisation and behaviour classifications (IMADGE), as well as two smaller annotated datasets for training/evaluating the identification (TIDe) and behaviour inference (ABODe) systems. This research constitutes the first of its kind in terms of the scale and challenges addressed. The data source (side-view single-channel video with clutter and no identification markers for mice) presents challenging conditions for analysis, but has the potential to give richer information while using industry standard housing.
A Tracking and Identification module was developed to automatically detect, track and identify the (visually similar) mice in the cluttered home-cage using only single-channel IR video and coarse position from RFID readings. Existing detectors and trackers were combined with a novel Integer Linear Programming formulation to assign anonymous tracks to mouse identities. This utilised a probabilistic weight model of affinity between detections and RFID pickups.
The next task necessitated the implementation of the Activity Labelling module that classifies the behaviour of each mouse, handling occlusion to avoid giving unreliable classifications when the mice cannot be observed. Two key aspects of this were (a) careful feature-selection, and (b) judicious balancing of the errors of the system in line with the repercussions for our setup.
Given these sequences of individual behaviours, we analysed the interaction dynamics between mice in the same cage by collapsing the group behaviour into a sequence of interpretable latent regimes using both static and temporal (Markov) models. Using a permutation matrix, we were able to automatically assign mice to roles in the HMM, fit a global model to a group of cages and analyse abnormalities in data from a different demographic
What Makes a Habitat a Home: Understanding Settlement and Recruitment Variation in European Sea Bass, Dicentrarchus labrax
Sea bass stocks in the UK are in decline as a result of increased fishing pressure and variable inter-annual recruitment. Recruitment variation is driven by survival in the early life stages; therefore, nursery habitats are thought to be able to stabilize recruitment through providing optimal growth conditions for juvenile fish. A thorough understanding of the factors that drive juvenile sea bass survival is needed, however, our understanding of what constitutes quality nursery habitat for juvenile sea bass is weak, with current knowledge based almost solely on saltmarshes. Juvenile sea bass were sampled using conventional seine and fyke nets across estuarine habitats, alongside dietary DNA metabarcoding to assess their distribution diet and condition, using measures of abundance, condition, stomach fullness, and diet. To determine whether the mechanism of larvae entering estuarine nurseries is an active or passive process the vertical distribution patterns of larval sea bass were compared across tidal cycles. Finally, over-winter survival was predicted based on energy budget modelling and temperature-dependent growth experiments, based on in-situ measurements of winter temperatures. Juvenile sea bass did not differentially select high tide habitats, but saltmarshes and sand provided increased foraging success. At low tide, however, sea bass were more abundant in complex habitat with lower foraging success. Diets mainly consisted of decapods and polychaete worms across habitats, but there was evidence of increased planktivory over mud. Larval sea bass did not show evidence of flood tide transport and likely rely on passive tidal forcing to migrate into estuaries, or they are trying to retain to deeper water. According to our models, winter thermal minima resulted in complete cohort loss in all scenarios on the East coast. The results of this study suggest that multiple habitats along the estuarine mosaic are important for juvenile sea bass at some point, and that a seascape approach to management is necessary, however, winter temperatures likely present a more extreme bottleneck to recruitment
Water Governance and Management Practices in the Republic of Ireland: Past, Present and the Future
Robust water governance and management practices are critical in safeguarding water resources against threats such as drought, water pollution, infrastructure deficits, population growth, and policy implementation challenges. Despite being susceptible to these challenges, the Republic of Ireland (RoI) has implemented reforms aimed at facilitating a more integrated national approach to water resource protection. Following a descriptive, concurrent mixed method approach and research lens, this study examines three key research questions, providing the first comprehensive evaluation of changes in water governance and practices in the water-rich RoI. The research highlights significant events and measures taken to prepare for future challenges
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