4 research outputs found
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 system鈥檚 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 generaci贸n de inundaciones, su control y
manejo, han sido tratados con herramientas tradicionales de modelado hidrol贸gico enfocados
al estudio y an谩lisis de la relaci贸n precipitaci贸n-escorrent铆a, proceso f铆sico que es impulsado
por el ciclo hidrol贸gico y el r茅gimen clim谩tico y este esta directamente proporcional a la
generaci贸n de crecidas. Dentro de la disciplina hidrol贸gica, clasifican estas herramientas
de modelado tradicionales en tres grupos principales, siendo el primer grupo el de modelos
emp铆ricos (modelos de caja negra), modelos conceptuales (o agrupados, semi-agrupados o
semi-distribuidos) dependiendo de la distribuci贸n espacial y, por 煤ltimo, los basados en la
f铆sica, modelos de proceso (o "modelos de caja blanca", y/o distribuidos). En este sentido,
clasifican las aplicaciones de predicci贸n de caudal fluvial en la ingenier铆a de recursos h铆dricos
en dos tipos con respecto a los valores y par谩metros que requieren en: modelos de procesos
basados en la f铆sica y la categor铆a de modelos impulsados por datos.
Los modelos basados en la f铆sica proporcionan una descripci贸n detallada de la din谩mica
relacionada con los aspectos f铆sicos que ocurren internamente entre los diferentes sistemas de
una cuenca hidrogr谩fica determinada. Sin embargo, aparte de ser complejos de implementar,
se basan completamente en algoritmos matem谩ticos, y la comprensi贸n de estas interacciones
requiere la abstracci贸n de conceptos matem谩ticos y la conceptualizaci贸n de los procesos
f铆sicos que se entrelazan entre estos sistemas. Adem谩s, los modelos impulsados por datos no
requieren conocimiento de los procesos f铆sicos que gobiernan, sino que se basan 煤nicamente
en ecuaciones emp铆ricas que necesitan una gran cantidad de datos y requieren calibraci贸n
de los datos en el sitio. Los dos modelos difieren significativamente debido a sus requisitos
de datos y de c贸mo expresan los fen贸menos f铆sicos. La elaboraci贸n de modelos hidrol贸gicos
para el pron贸stico de inundaciones ha dado grandes pasos, pero siguen sin resolverse algunos
contratiempos importantes, dada la naturaleza estoc谩stica de los fen贸menos hidrol贸gicos, es
el desaf铆o de implementar sistemas de pron贸stico f谩ciles de usar, reutilizables, robustos y
confiables, la cantidad de incertidumbre que deben afrontar al intentar resolver el problema
de la predicci贸n de inundaciones. Sin embargo, en las 煤ltimas d茅cadas, con el entorno
creciente y el desarrollo del campo de la inteligencia artificial (IA), algunos investigadores
rara vez han intentado abordar la naturaleza estoc谩stica de los eventos hidrol贸gicos con la
aplicaci贸n de algunas de estas t茅cnicas.
Dados los contratiempos en el pron贸stico de inundaciones hidrol贸gicas descritos anteriormente,
esta investigaci贸n de tesis tiene como objetivo integrar los modelos hidrol贸gicos,
basados en la f铆sica, hidr谩ulicos e impulsados por datos bajo el paradigma de Sistemas de m煤ltiples agentes para el pron贸stico de inundaciones por medio del bosquejo y desarrollo
del marco de trabajo del sistema multi-agente (MAS) para los eventos de predicci贸n de
inundaciones en el contexto de cuenca hidrogr谩fica tropical.
Con la aparici贸n de las tecnolog铆as de agentes, se han emprendido algunos enfoques
de simulaci贸n recientes en la investigaci贸n hidrol贸gica con modelos basados en agentes y
sistema multi-agente, principalmente en alerta por inundaciones, seguridad y planificaci贸n
de inundaciones, control y gesti贸n de inundaciones y pron贸stico de inundaciones, todos estos
enfocado a simulacros de evacuaci贸n, y este 煤ltimo no dirigido a la cuenca tropical, cuyo
r茅gimen hidrol贸gico es extremadamente 煤nico.
En este enfoque de entorno de modelado de cuencas, se aplican los enfoques de sistemas
multi-agente como un sustituto del modelado hidrol贸gico convencional para construir un
sistema que opera a nivel de cuenca con estaciones hidrom茅tricas desplegadas, que utilizan
los datos de redes de sensores hidrom茅tricos (por ejemplo, lluvia , nivel del r铆o, caudal del
r铆o) capturado, almacenado y administrado por una organizaci贸n de agentes interactuantes
cuyo objetivo principal es realizar pron贸sticos de caudal y concientizaci贸n para mejorar las
capacidades de soporte en la formulaci贸n de pol铆ticas a nivel de cuenca hidrogr谩fica.
La primera secci贸n de este documento analiza el estado del arte sobre la investigaci贸n actual
en modelos hidrol贸gicos para la tarea de pron贸stico de inundaciones. Es un viaje a trav茅s
de los antecedentes preocupantes relacionadas con el proceso hidrol贸gico, las ontolog铆as de
inundaciones, la gesti贸n y la predicci贸n. El apartado abarca, en cierta medida, las t茅cnicas,
m茅todos y aspectos te贸ricos y m茅todos del modelado hidrol贸gico y sus tipolog铆as, desde
los modelos convencionales hasta los prototipos de inteligencia artificial actuales, haciendo
hincapi茅 en los sistemas multi-agente, como un enfoque de simulaci贸n reciente en la investigaci贸n
hidrol贸gica. Sin embargo, se destaca que esta secci贸n no contribuye a una revisi贸n
integral, sino que su prop贸sito es servir de marco para este tipo de trabajos y una gu铆a para
subrayar los aspectos significativos de los trabajos.
En la secci贸n dos del documento, se detalla el marco de trabajo propuesto para el sistema
multi-agente para el pron贸stico de inundaciones. Los trabajos realizados comprendieron el
dise帽o y desarrollo del marco de trabajo del sistema multi-agente con la arquitectura (modelo
Creencia-Deseo-Intenci贸n) para la predicci贸n de eventos de crecidas dentro del concepto
de cuenca hidrogr谩fica tropical. Las contribuciones de esta arquitectura propuesta son el
reemplazo del modelado hidrol贸gico convencional con el uso de sistemas multi-agente, lo
que agiliza la administraci贸n de las series de tiempo de datos hidrom茅tricos y el modelado
del proceso de precipitaci贸n-escorrent铆a que conduce a la inundaci贸n en el curso de un r铆o.
Otra ventaja es el entorno amigable proporcionado por la interfaz gr谩fica de la plataforma del
sistema multi-agente propuesto, la generaci贸n en tiempo real de gr谩ficos, cuadros y monitores
con la informaci贸n sobre el evento inmediato que tiene lugar en la cuenca, lo que lo hace
f谩cil para el espectador con algo o sin experiencia en an谩lisis de datos y su interpretaci贸n
para tener una idea visual de la informaci贸n disponible con respecto a la cognici贸n de las
inundaciones.
Los agentes necesarios desarrollados en este marco de modelado de sistemas multi-agente
para el pron贸stico de inundaciones han sido entrenados, probados y validados en una serie de tareas experimentales, utilizando la informaci贸n de la serie hidrom茅trica de datos de lluvia,
nivel del r铆o y flujo del curso de agua recolectados por los agentes sensores hidrom茅tricos de
los sensores hidrom茅tricos de campo.Programa de Doctorado en Ciencia y Tecnolog铆a Inform谩tica por la Universidad Carlos III de MadridPresidente: Mar铆a Araceli Sanchis de Miguel.- Secretario: Juan G贸mez Romero.- Vocal: Juan Carlos Corrale
Automated Multi-Agent Simulation Generation and Validation
International audienceMulti-agent based simulation (MABS) is increasingly used for so- cial science studies. However, few methodologies and tools ex- ist. A strong issue is the choice of the number of simulation runs and the validation of the results by statistical methods. In this arti- cle, we propose a model of tool which automatically generates and runs new simulations until the results are statistically valid using a chi-square test. The choice of the test con铿乬uration allows both a general overview of the variable links and a more speci铿乧 inde- pendence analysis. We present a generic tool for any RePast-based simulation and apply it on an Academic Labor Market economic simulation
Automated Multi-Agent Simulation Generation and Validation
International audienceMulti-agent based simulation (MABS) is increasingly used for so- cial science studies. However, few methodologies and tools ex- ist. A strong issue is the choice of the number of simulation runs and the validation of the results by statistical methods. In this arti- cle, we propose a model of tool which automatically generates and runs new simulations until the results are statistically valid using a chi-square test. The choice of the test con铿乬uration allows both a general overview of the variable links and a more speci铿乧 inde- pendence analysis. We present a generic tool for any RePast-based simulation and apply it on an Academic Labor Market economic simulation