19,430 research outputs found
Defining the hundred year flood: a Bayesian approach for using historic data to reduce uncertainty in flood frequency estimates
This paper describes a Bayesian statistical model for estimating flood frequency by combining uncertain annual maximum (AMAX) data from a river gauge with estimates of flood peak discharge from various historic sources that predate the period of instrument records. Such historic flood records promise to expand the time series data needed for reducing the uncertainty in return period estimates for extreme events, but the heterogeneity and uncertainty of historic records make them difficult to use alongside Flood Estimation Handbook and other standard methods for generating flood frequency curves from gauge data. Using the flow of the River Eden in Carlisle, Cumbria, UK as a case study, this paper develops a Bayesian model for combining historic flood estimates since 1800 with gauge data since 1967 to estimate the probability of low frequency flood events for the area taking account of uncertainty in the discharge estimates. Results show a reduction in 95% confidence intervals of roughly 50% for annual exceedance probabilities of less than 0.0133 (return periods over 75 years) compared to standard flood frequency estimation methods using solely systematic data. Sensitivity analysis shows the model is sensitive to 2 model parameters both of which are concerned with the historic (pre-systematic) period of the time series. This highlights the importance of adequate consideration of historic channel and floodplain changes or possible bias in estimates of historic flood discharges. The next steps required to roll out this Bayesian approach for operational flood frequency estimation at other sites is also discussed
Decision support systems for large dam planning and operation in Africa
Decision support systems/ Dams/ Planning/ Operations/ Social impact/ Environmental effects
Tortious Toxics
In this Article we offer one small idea with potentially large implications. We propose the recognition arid development of a special tort for toxic exposures, where the exposures have not yet led to a physical illness such as cancer. We argue, in brief, that this new tort would, in one simple step, accomplish three things: it would address many of the problems with the courts\u27 current handling of toxic torts; it would consolidate the many overlapping causes of action now pressed in toxic tort cases into one single claim; and it would give expression to the real injury motivating these cases - a dignitary and autonomy-based harm, not a physical one
Multi-agent system for flood forecasting in Tropical River Basin
It is well known, the problems related to the generation of floods, their control, and management,
have been treated with traditional hydrologic modeling tools focused on the study and
the analysis of the precipitation-runoff relationship, a physical process which is driven by the
hydrological cycle and the climate regime and that is directly proportional to the generation
of floodwaters. Within the hydrological discipline, they classify these traditional modeling
tools according to three principal groups, being the first group defined as trial-and-error models
(e.g., "black-models"), the second group are the conceptual models, which are categorized
in three main sub-groups as "lumped", "semi-lumped" and "semi-distributed", according to
the special distribution, and finally, models that are based on physical processes, known as
"white-box models" are the so-called "distributed-models". On the other hand, in engineering
applications, there are two types of models used in streamflow forecasting, and which are
classified concerning the type of measurements and variables required as "physically based
models", as well as "data-driven models".
The Physically oriented prototypes present an in-depth account of the dynamics related
to the physical aspects that occur internally among the different systems of a given hydrographic
basin. However, aside from being laborious to implement, they rely thoroughly
on mathematical algorithms, and an understanding of these interactions requires the abstraction
of mathematical concepts and the conceptualization of the physical processes that
are intertwined among these systems. Besides, models determined by data necessitates an
a-priori understanding of the physical laws controlling the process within the system, and
they are bound to mathematical formulations, which require a lot of numeric information
for field adjustments. Therefore, these models are remarkably different from each other
because of their needs for data, and their interpretation of physical phenomena. Although
there is considerable progress in hydrologic modeling for flood forecasting, several significant
setbacks remain unresolved, given the stochastic nature of the hydrological phenomena, is
the challenge to implement user-friendly, re-usable, robust, and reliable forecasting systems,
the amount of uncertainty they must deal with when trying to solve the flood forecasting
problem. However, in the past decades, with the growing environment and development of
the artificial intelligence (AI) field, some researchers have seldomly attempted to deal with
the stochastic nature of hydrologic events with the application of some of these techniques.
Given the setbacks to hydrologic flood forecasting previously described this thesis research
aims to integrate the physics-based hydrologic, hydraulic, and data-driven models under the
paradigm of Multi-agent Systems for flood forecasting by designing and developing a multi-agent system (MAS) framework for flood forecasting events within the scope of tropical
watersheds.
With the emergence of the agent technologies, the "agent-based modeling" and "multiagent
systems" simulation methods have provided applications for some areas of hydro base
management like flood protection, planning, control, management, mitigation, and forecasting
to combat the shocks produced by floods on society; however, all these focused on
evacuation drills, and the latter not aimed at the tropical river basin, whose hydrological
regime is extremely unique.
In this catchment modeling environment approach, it was applied the multi-agent systems
approach as a surrogate of the conventional hydrologic model to build a system that operates
at the catchment level displayed with hydrometric stations, that use the data from hydrometric
sensors networks (e.g., rainfall, river stage, river flow) captured, stored and administered
by an organization of interacting agents whose main aim is to perform flow forecasting and
awareness, and in so doing enhance the policy-making process at the watershed level.
Section one of this document surveys the status of the current research in hydrologic
modeling for the flood forecasting task. It is a journey through the background of related
concerns to the hydrological process, flood ontologies, management, and forecasting. The
section covers, to a certain extent, the techniques, methods, and theoretical aspects and
methods of hydrological modeling and their types, from the conventional models to the
present-day artificial intelligence prototypes, making special emphasis on the multi-agent
systems, as most recent modeling methodology in the hydrological sciences. However, it is
also underlined here that the section does not contribute to an all-inclusive revision, rather
its purpose is to serve as a framework for this sort of work and a path to underline the
significant aspects of the works.
In section two of the document, it is detailed the conceptual framework for the suggested
Multiagent system in support of flood forecasting. To accomplish this task, several works
need to be carried out such as the sketching and implementation of the systemâs framework
with the (Belief-Desire-Intention model) architecture for flood forecasting events within the
concept of the tropical river basin. Contributions of this proposed architecture are the
replacement of the conventional hydrologic modeling with the use of multi-agent systems,
which makes it quick for hydrometric time-series data administration and modeling of the
precipitation-runoff process which conveys to flood in a river course. Another advantage is
the user-friendly environment provided by the proposed multi-agent system platform graphical
interface, the real-time generation of graphs, charts, and monitors with the information
on the immediate event taking place in the catchment, which makes it easy for the viewer
with some or no background in data analysis and their interpretation to get a visual idea of
the information at hand regarding the flood awareness.
The required agents developed in this multi-agent system modeling framework for flood
forecasting have been trained, tested, and validated under a series of experimental tasks,
using the hydrometric series information of rainfall, river stage, and streamflow data collected
by the hydrometric sensor agents from the hydrometric sensors.Como se sabe, los problemas relacionados con la generaciĂłn de inundaciones, su control y
manejo, han sido tratados con herramientas tradicionales de modelado hidrolĂłgico enfocados
al estudio y anĂĄlisis de la relaciĂłn precipitaciĂłn-escorrentĂa, proceso fĂsico que es impulsado
por el ciclo hidrológico y el régimen climåtico y este esta directamente proporcional a la
generaciĂłn de crecidas. Dentro de la disciplina hidrolĂłgica, clasifican estas herramientas
de modelado tradicionales en tres grupos principales, siendo el primer grupo el de modelos
empĂricos (modelos de caja negra), modelos conceptuales (o agrupados, semi-agrupados o
semi-distribuidos) dependiendo de la distribuciĂłn espacial y, por Ășltimo, los basados en la
fĂsica, modelos de proceso (o "modelos de caja blanca", y/o distribuidos). En este sentido,
clasifican las aplicaciones de predicciĂłn de caudal fluvial en la ingenierĂa de recursos hĂdricos
en dos tipos con respecto a los valores y parĂĄmetros que requieren en: modelos de procesos
basados en la fĂsica y la categorĂa de modelos impulsados por datos.
Los modelos basados en la fĂsica proporcionan una descripciĂłn detallada de la dinĂĄmica
relacionada con los aspectos fĂsicos que ocurren internamente entre los diferentes sistemas de
una cuenca hidrogrĂĄfica determinada. Sin embargo, aparte de ser complejos de implementar,
se basan completamente en algoritmos matemĂĄticos, y la comprensiĂłn de estas interacciones
requiere la abstracciĂłn de conceptos matemĂĄticos y la conceptualizaciĂłn de los procesos
fĂsicos que se entrelazan entre estos sistemas. AdemĂĄs, los modelos impulsados por datos no
requieren conocimiento de los procesos fĂsicos que gobiernan, sino que se basan Ășnicamente
en ecuaciones empĂricas que necesitan una gran cantidad de datos y requieren calibraciĂłn
de los datos en el sitio. Los dos modelos difieren significativamente debido a sus requisitos
de datos y de cĂłmo expresan los fenĂłmenos fĂsicos. La elaboraciĂłn de modelos hidrolĂłgicos
para el pronĂłstico de inundaciones ha dado grandes pasos, pero siguen sin resolverse algunos
contratiempos importantes, dada la naturaleza estocĂĄstica de los fenĂłmenos hidrolĂłgicos, es
el desafĂo de implementar sistemas de pronĂłstico fĂĄciles de usar, reutilizables, robustos y
confiables, la cantidad de incertidumbre que deben afrontar al intentar resolver el problema
de la predicciĂłn de inundaciones. Sin embargo, en las Ășltimas dĂ©cadas, con el entorno
creciente y el desarrollo del campo de la inteligencia artificial (IA), algunos investigadores
rara vez han intentado abordar la naturaleza estocĂĄstica de los eventos hidrolĂłgicos con la
aplicación de algunas de estas técnicas.
Dados los contratiempos en el pronĂłstico de inundaciones hidrolĂłgicas descritos anteriormente,
esta investigaciĂłn de tesis tiene como objetivo integrar los modelos hidrolĂłgicos,
basados en la fĂsica, hidrĂĄulicos e impulsados por datos bajo el paradigma de Sistemas de mĂșltiples agentes para el pronĂłstico de inundaciones por medio del bosquejo y desarrollo
del marco de trabajo del sistema multi-agente (MAS) para los eventos de predicciĂłn de
inundaciones en el contexto de cuenca hidrogrĂĄfica tropical.
Con la apariciĂłn de las tecnologĂas de agentes, se han emprendido algunos enfoques
de simulaciĂłn recientes en la investigaciĂłn hidrolĂłgica con modelos basados en agentes y
sistema multi-agente, principalmente en alerta por inundaciones, seguridad y planificaciĂłn
de inundaciones, control y gestiĂłn de inundaciones y pronĂłstico de inundaciones, todos estos
enfocado a simulacros de evacuaciĂłn, y este Ășltimo no dirigido a la cuenca tropical, cuyo
rĂ©gimen hidrolĂłgico es extremadamente Ășnico.
En este enfoque de entorno de modelado de cuencas, se aplican los enfoques de sistemas
multi-agente como un sustituto del modelado hidrolĂłgico convencional para construir un
sistema que opera a nivel de cuenca con estaciones hidrométricas desplegadas, que utilizan
los datos de redes de sensores hidromĂ©tricos (por ejemplo, lluvia , nivel del rĂo, caudal del
rĂo) capturado, almacenado y administrado por una organizaciĂłn de agentes interactuantes
cuyo objetivo principal es realizar pronĂłsticos de caudal y concientizaciĂłn para mejorar las
capacidades de soporte en la formulaciĂłn de polĂticas a nivel de cuenca hidrogrĂĄfica.
La primera secciĂłn de este documento analiza el estado del arte sobre la investigaciĂłn actual
en modelos hidrológicos para la tarea de pronóstico de inundaciones. Es un viaje a través
de los antecedentes preocupantes relacionadas con el proceso hidrolĂłgico, las ontologĂas de
inundaciones, la gestión y la predicción. El apartado abarca, en cierta medida, las técnicas,
mĂ©todos y aspectos teĂłricos y mĂ©todos del modelado hidrolĂłgico y sus tipologĂas, desde
los modelos convencionales hasta los prototipos de inteligencia artificial actuales, haciendo
hincapié en los sistemas multi-agente, como un enfoque de simulación reciente en la investigación
hidrolĂłgica. Sin embargo, se destaca que esta secciĂłn no contribuye a una revisiĂłn
integral, sino que su propĂłsito es servir de marco para este tipo de trabajos y una guĂa para
subrayar los aspectos significativos de los trabajos.
En la secciĂłn dos del documento, se detalla el marco de trabajo propuesto para el sistema
multi-agente para el pronĂłstico de inundaciones. Los trabajos realizados comprendieron el
diseño y desarrollo del marco de trabajo del sistema multi-agente con la arquitectura (modelo
Creencia-Deseo-IntenciĂłn) para la predicciĂłn de eventos de crecidas dentro del concepto
de cuenca hidrogrĂĄfica tropical. Las contribuciones de esta arquitectura propuesta son el
reemplazo del modelado hidrolĂłgico convencional con el uso de sistemas multi-agente, lo
que agiliza la administración de las series de tiempo de datos hidrométricos y el modelado
del proceso de precipitaciĂłn-escorrentĂa que conduce a la inundaciĂłn en el curso de un rĂo.
Otra ventaja es el entorno amigable proporcionado por la interfaz grĂĄfica de la plataforma del
sistema multi-agente propuesto, la generaciĂłn en tiempo real de grĂĄficos, cuadros y monitores
con la informaciĂłn sobre el evento inmediato que tiene lugar en la cuenca, lo que lo hace
fĂĄcil para el espectador con algo o sin experiencia en anĂĄlisis de datos y su interpretaciĂłn
para tener una idea visual de la informaciĂłn disponible con respecto a la cogniciĂłn de las
inundaciones.
Los agentes necesarios desarrollados en este marco de modelado de sistemas multi-agente
para el pronóstico de inundaciones han sido entrenados, probados y validados en una serie de tareas experimentales, utilizando la información de la serie hidrométrica de datos de lluvia,
nivel del rĂo y flujo del curso de agua recolectados por los agentes sensores hidromĂ©tricos de
los sensores hidromĂ©tricos de campo.Programa de Doctorado en Ciencia y TecnologĂa InformĂĄtica por la Universidad Carlos III de MadridPresidente: MarĂa Araceli Sanchis de Miguel.- Secretario: Juan GĂłmez Romero.- Vocal: Juan Carlos Corrale
Estimating Fire Weather Indices via Semantic Reasoning over Wireless Sensor Network Data Streams
Wildfires are frequent, devastating events in Australia that regularly cause
significant loss of life and widespread property damage. Fire weather indices
are a widely-adopted method for measuring fire danger and they play a
significant role in issuing bushfire warnings and in anticipating demand for
bushfire management resources. Existing systems that calculate fire weather
indices are limited due to low spatial and temporal resolution. Localized
wireless sensor networks, on the other hand, gather continuous sensor data
measuring variables such as air temperature, relative humidity, rainfall and
wind speed at high resolutions. However, using wireless sensor networks to
estimate fire weather indices is a challenge due to data quality issues, lack
of standard data formats and lack of agreement on thresholds and methods for
calculating fire weather indices. Within the scope of this paper, we propose a
standardized approach to calculating Fire Weather Indices (a.k.a. fire danger
ratings) and overcome a number of the challenges by applying Semantic Web
Technologies to the processing of data streams from a wireless sensor network
deployed in the Springbrook region of South East Queensland. This paper
describes the underlying ontologies, the semantic reasoning and the Semantic
Fire Weather Index (SFWI) system that we have developed to enable domain
experts to specify and adapt rules for calculating Fire Weather Indices. We
also describe the Web-based mapping interface that we have developed, that
enables users to improve their understanding of how fire weather indices vary
over time within a particular region.Finally, we discuss our evaluation results
that indicate that the proposed system outperforms state-of-the-art techniques
in terms of accuracy, precision and query performance.Comment: 20pages, 12 figure
Policy Analysis for Natural Hazards: Some Cautionary Lessons From Environmental Policy Analysis
How should agencies and legislatures evaluate possible policies to mitigate the impacts of earthquakes, floods, hurricanes and other natural hazards? In particular, should governmental bodies adopt the sorts of policy-analytic and risk assessment techniques that are widely used in the area of environmental hazards (chemical toxins and radiation)? Environmental hazards policy analysis regularly employs proxy tests, in particular tests of technological feasibility, rather than focusing on a policy\u27s impact on well-being. When human welfare does enter the analysis, particular aspects of well-being, such as health and safety, are often given priority over others. Individual risk tests and other features of environmental policy analysis sometimes make policy choice fairly insensitive to the size of the exposed population. Seemingly arbitrary numerical cutoffs, such as the one-in-one million incremental risk level, help structure policy evaluation. Risk assessment techniques are often deterministic rather than probabilistic, and in estimating point values often rely on conservative rather than central-tendency estimates. The Article argues that these sorts of features of environmental policy analysis may be justifiable, but only on institutional grounds-if they sufficiently reduce decision costs or bureaucratic error or shirking-and should not be reflexively adopted by natural hazards policymakers. Absent persuasive. institutional justification, natural hazards policy analysis should be welfare-focused, multidimensional, and sensitive to population size, and natural hazards risk assessment techniques should provide information suitable for policy-analytic techniques of this sort
Policy Analysis for Natural Hazards: Some Cautionary Lessons From Environmental Policy Analysis
How should agencies and legislatures evaluate possible policies to mitigate the impacts of earthquakes, floods, hurricanes and other natural hazards? In particular, should governmental bodies adopt the sorts of policy-analytic and risk assessment techniques that are widely used in the area of environmental hazards (chemical toxins and radiation)? Environmental hazards policy analysis regularly employs proxy tests, in particular tests of technological feasibility, rather than focusing on a policy\u27s impact on well-being. When human welfare does enter the analysis, particular aspects of well-being, such as health and safety, are often given priority over others. Individual risk tests and other features of environmental policy analysis sometimes make policy choice fairly insensitive to the size of the exposed population. Seemingly arbitrary numerical cutoffs, such as the one-in-one million incremental risk level, help structure policy evaluation. Risk assessment techniques are often deterministic rather than probabilistic, and in estimating point values often rely on conservative rather than central-tendency estimates. The Article argues that these sorts of features of environmental policy analysis may be justifiable, but only on institutional grounds-if they sufficiently reduce decision costs or bureaucratic error or shirking-and should not be reflexively adopted by natural hazards policymakers. Absent persuasive. institutional justification, natural hazards policy analysis should be welfare-focused, multidimensional, and sensitive to population size, and natural hazards risk assessment techniques should provide information suitable for policy-analytic techniques of this sort
Adapting to change: Time for climate resilience and a new adaptation strategy. EPC Issue Paper 5 March 2020
The dramatic effects of climate change are being felt across the European continent and the world. Considering how sluggish and unsuccessful the world has been in reducing greenhouse gas (GHG) emissions, the impacts will become long-lasting scars. Even implementing radical climate mitigation now would be insufficient in addressing the economic, societal and environmental implications of climate change, which are expected to only intensify in the years to come.
This means climate mitigation must go hand in hand with the adaptation efforts recognised in the Paris Agreement. And although the damages of climate change are usually localised and adaptation measures often depend on local specificities, given the interconnections between ecosystems, people and economies in a globalised world there are strong reasons for European Union (EU) member states to join forces, pool risk and cooperate across borders. Sharing information, good practices, experiences and resources to strengthen resilience and enhance adaptive capacity makes sense economically, environmentally and socially.
The European Commissionâs 2013 Adaptation Strategy is the first attempt to set EU-wide adaptation and climate resilience and could be considered novel in that it tried to mainstream adaptation goals into relevant legislation, instruments and funds. It was not very proactive, however. It also lacked long-term perspective, failed to put the adaptation file high on the political agenda, was under resourced, and suffered from knowledge gaps and silo thinking.
The Commissionâs European Green Deal proposal, which has been presented as a major step forward to the goal of Europe becoming the worldâs first climate-neutral continent, suggests that the Commission will adopt a new EU strategy on adaptation to climate within the first two years of its mandate (2020-2021). In light of the
risks climate change poses to ecosystems, societies and the economy (through inter alia the vulnerability of the supply chain to climate change and its potential failure to provide services to consumers), adaptation should take a prominent role alongside mitigation in the EUâs political climate agenda.
Respecting the division of treaty competences, there are important areas where EU-wide action and support could foster the continentâs resilience to climate change. The European Policy Centre (EPC) project âBuilding a climate-resilient Europeâ, which has culminated in this Issue Paper, has identified the following: (i) the ability to convert science-based knowledge into preventive action and responsible behaviour, thus filling the information gap; (ii) the need to close the protection gap through better risk management and risk sharing; (iii) the necessity to adopt nature-based infrastructural solutions widely and tackle the grey infrastructure bias; and (iv) the need to address the funding and investment gap.
This Issue Paper aims to help inform the upcoming EU Adaptation Strategy and, by extension, strengthen the EUâs resilience to climate change. To that end, the authors make a call for the EU to mainstream adaptation and shift its focus from reacting to disasters to a more proactive approach that prioritises prevention, risk reduction and resilience building. In doing so, the EU must ensure fairness and distributive justice while striving for climate change mitigation and protecting the environment and biodiversity.
To succeed, the new EU Adaptation Strategy will need to address specific challenges related to the information, protection, funding and investment gaps; and the grey infrastructure bias. To tackle and address those challenges, this Paper proposes 17 solutions outlined in Table 1 (see page 6)
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