OpenSDM - An Open Sensor Data Management Tool

Exchange of scientific data and metadata between single users or organizations is a challenging task due to differences in data formats, the genesis of data collection, ontologies and prior knowledge of the users. Different data storage requirements, mostly defined by the structure, size and access scenarios, require also different data storage solutions, since there is no and there cannot be a data format which is suitable for all tasks and needs that occur especially in a scientific workflow. Besides data, the generation and handling of additional corresponding metadata leads us to the additional challenge of defining the meaning of data, which should be formulated in a way that it can be commonly understood to get out a maximum of expected and shareable information of the observed processes. In our domain we are able to take advantage of standards defined by the Open Geospatial Consortium, namely the standards defined by the Sensor Web Enablement, WaterML and CF-NetCDF working groups. Even though these standards are freely available and some of them are commonly used in specialized software packages, the adaption in widespread end-user software solutions still seems to be in its beginnings. This contribution describes a software solution developed at Graz University of Technology, which targets the storage and exchange of measurement data with a special focus on meteorological, water quantity and water quality observation data collected within the last three decades. The solution was planned on basis of long-term experience in sewer monitoring and was built on top of open-source software only. It allows high-performance storage of time series and associated metadata, access-controlled web services for programmatic access, validation tasks, event detection, automated alerting and notification. An additional web-based graphical user interface was created which gives full control to end-users. The OpenSDM software approach makes it easier for measurement station operators, maintainers and end-users to take advantage of the standards of the Open Geospatial Consortium, which usage should be promoted in the water related communities

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’s 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 generación de inundaciones, su control y manejo, han sido tratados con herramientas tradicionales de modelado hidrológico enfocados al estudio y análisis de la relación precipitación-escorrentía, proceso físico que es impulsado por el ciclo hidrológico y el régimen climático y este esta directamente proporcional a la generación de crecidas. Dentro de la disciplina hidrológica, clasifican estas herramientas de modelado tradicionales en tres grupos principales, siendo el primer grupo el de modelos empíricos (modelos de caja negra), modelos conceptuales (o agrupados, semi-agrupados o semi-distribuidos) dependiendo de la distribución espacial y, por último, los basados en la física, modelos de proceso (o "modelos de caja blanca", y/o distribuidos). En este sentido, clasifican las aplicaciones de predicción de caudal fluvial en la ingeniería de recursos hídricos en dos tipos con respecto a los valores y parámetros que requieren en: modelos de procesos basados en la física y la categoría de modelos impulsados por datos. Los modelos basados en la física proporcionan una descripción detallada de la dinámica relacionada con los aspectos físicos que ocurren internamente entre los diferentes sistemas de una cuenca hidrográfica determinada. Sin embargo, aparte de ser complejos de implementar, se basan completamente en algoritmos matemáticos, y la comprensión de estas interacciones requiere la abstracción de conceptos matemáticos y la conceptualización de los procesos físicos que se entrelazan entre estos sistemas. Además, los modelos impulsados por datos no requieren conocimiento de los procesos físicos que gobiernan, sino que se basan únicamente en ecuaciones empíricas que necesitan una gran cantidad de datos y requieren calibración de los datos en el sitio. Los dos modelos difieren significativamente debido a sus requisitos de datos y de cómo expresan los fenómenos físicos. La elaboración de modelos hidrológicos para el pronóstico de inundaciones ha dado grandes pasos, pero siguen sin resolverse algunos contratiempos importantes, dada la naturaleza estocástica de los fenómenos hidrológicos, es el desafío de implementar sistemas de pronóstico fáciles de usar, reutilizables, robustos y confiables, la cantidad de incertidumbre que deben afrontar al intentar resolver el problema de la predicción de inundaciones. Sin embargo, en las últimas décadas, con el entorno creciente y el desarrollo del campo de la inteligencia artificial (IA), algunos investigadores rara vez han intentado abordar la naturaleza estocástica de los eventos hidrológicos con la aplicación de algunas de estas técnicas. Dados los contratiempos en el pronóstico de inundaciones hidrológicas descritos anteriormente, esta investigación de tesis tiene como objetivo integrar los modelos hidrológicos, basados en la física, hidráulicos e impulsados por datos bajo el paradigma de Sistemas de múltiples agentes para el pronóstico de inundaciones por medio del bosquejo y desarrollo del marco de trabajo del sistema multi-agente (MAS) para los eventos de predicción de inundaciones en el contexto de cuenca hidrográfica tropical. Con la aparición de las tecnologías de agentes, se han emprendido algunos enfoques de simulación recientes en la investigación hidrológica con modelos basados en agentes y sistema multi-agente, principalmente en alerta por inundaciones, seguridad y planificación de inundaciones, control y gestión de inundaciones y pronóstico de inundaciones, todos estos enfocado a simulacros de evacuación, y este último no dirigido a la cuenca tropical, cuyo régimen hidrológico es extremadamente único. En este enfoque de entorno de modelado de cuencas, se aplican los enfoques de sistemas multi-agente como un sustituto del modelado hidrológico convencional para construir un sistema que opera a nivel de cuenca con estaciones hidrométricas desplegadas, que utilizan los datos de redes de sensores hidrométricos (por ejemplo, lluvia , nivel del río, caudal del río) capturado, almacenado y administrado por una organización de agentes interactuantes cuyo objetivo principal es realizar pronósticos de caudal y concientización para mejorar las capacidades de soporte en la formulación de políticas a nivel de cuenca hidrográfica. La primera sección de este documento analiza el estado del arte sobre la investigación actual en modelos hidrológicos para la tarea de pronóstico de inundaciones. Es un viaje a través de los antecedentes preocupantes relacionadas con el proceso hidrológico, las ontologías de inundaciones, la gestión y la predicción. El apartado abarca, en cierta medida, las técnicas, métodos y aspectos teóricos y métodos del modelado hidrológico y sus tipologías, 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 hidrológica. Sin embargo, se destaca que esta sección no contribuye a una revisión integral, sino que su propósito es servir de marco para este tipo de trabajos y una guía para subrayar los aspectos significativos de los trabajos. En la sección dos del documento, se detalla el marco de trabajo propuesto para el sistema multi-agente para el pronóstico de inundaciones. Los trabajos realizados comprendieron el diseño y desarrollo del marco de trabajo del sistema multi-agente con la arquitectura (modelo Creencia-Deseo-Intención) para la predicción de eventos de crecidas dentro del concepto de cuenca hidrográfica tropical. Las contribuciones de esta arquitectura propuesta son el reemplazo del modelado hidrológico 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 precipitación-escorrentía que conduce a la inundación en el curso de un río. Otra ventaja es el entorno amigable proporcionado por la interfaz gráfica de la plataforma del sistema multi-agente propuesto, la generación en tiempo real de gráficos, cuadros y monitores con la información sobre el evento inmediato que tiene lugar en la cuenca, lo que lo hace fácil para el espectador con algo o sin experiencia en análisis de datos y su interpretación para tener una idea visual de la información disponible con respecto a la cognición 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 río y flujo del curso de agua recolectados por los agentes sensores hidrométricos de los sensores hidrométricos de campo.Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: María Araceli Sanchis de Miguel.- Secretario: Juan Gómez Romero.- Vocal: Juan Carlos Corrale

Knowledge-based approaches for river basin management

International audienceRare attempts to use knowledge technologies and other relevant approaches are found in the river basin management. Some applications of expert systems as well as utilization of soft computing techniques (as neural networks or genetic algorithms) are known in an experimental level. Knowledge management approaches still have not been used at all. In this paper we discuss knowledge-based approaches in the river basin management as a difficult yet important direction which could be proven to be helpful. We summarize the research done in the scope of the AQUIN project, one of first Czech knowledge management projects in the river basin management. The project was initiated by the water management company in Pilsen, where dispatchers make decisions about manipulations on the reservoir Nýrsko, the strategic source of drinking water for inhabitants of Pilsen. The project aim was to support dispatchers' decision making under a high degree of uncertainty or data shortage. The research is continued in the scope of a new project AQUINpro, planned for the period of 2006 to 2008

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

AUTOMATIC ASSESSMENT OF LAKE STATUS USING AN OPEN SOURCE APPROACH: LAKE LUGANO'S CASE STUDY

Abstract. Climate change and human activities are increasingly threatening water resources. In particular sub-alpine lakes are fundamental not only for tourism or other economical activities, but also as a source of water. In this context, there is a strong need to monitor such resources to understand, study and react to known and unknown impacts, so that appropriate mitigation actions can be taken. Unfortunately, although monitoring data already exist for many of these lakes, the information is archived in different formats and servers undermining the full exploitation of data and preventing a more efficient data management. The aim of this work is to improve this situation by implementing a system that integrates and standardizes data coming from different sources. In addition, the system integrates web based tools that estimate lake state indicators using open source software and standard. Thanks to this system, it will be possible to exploit the data potential more fully. This paper focuses on the achievements reached by the research carried out on Lake Lugano in the context of the project SIMILE after two years of work

A Holistic ICT Solution to Improve Matching between Supply and Demand over the Water Supply Distribution Chain

While many water management tools exist, these systems are not usually interconnected and therefore cannot communicate between one another, preventing Integrated Water Resources Management to be fully achieved. This paper presents the solution proposed by WatERP project* where a novel solution enables better matching between water supply and demand from holistic perspective. Subsystems that control the production, management and consumption of water will be interconnected through both information architecture and intelligent infrastructure. The main outcome will consist of, a web-based Open Management Platform integrating near real-time knowledge on water supplies and demand, from sources to users, across geographic and organizational scales and supported by a knowledge base where information will be structured in water management ontology to ensure interoperability and maximize usability. WatERP will thus provide a major contribution to: 1) Improve coordination among actors, 2) Foster behavioural change, 3) Reduce water and energy consumption, 4) Optimize water accountability

How can the Data Revolution contribute to climate action in smallholder agriculture?

In this article, we discuss the ongoing Data Revolution in relation to climate action in agriculture. Data are highly relevant for climate action, as climate change makes current local knowledge increasingly irrelevant and requires smarter management of agricultural systems. We discuss five datarelated concepts and explore how they are linked with agricultural climate action: lean data, crowdsourcing, big data, ubiquitous computing, and information design. We show practical examples for each of these concepts. There are many opportunities for improving agricultural development projects, providing new services to smallholder farmers, and generating better information for policy- and decision-making. Making the Data Revolution work for smallholder farmers’ climate action not only takes further technological development, but also requires careful governance and public investment to avoid a few actors taking over the current innovation space and stifle further development

Probability assessment of flood and sediment disasters in Japan using the Total Runoff-Integrating Pathways model

AbstractTo address many of the problems faced in hydrological engineering planning, design, and management, a detailed knowledge of flood event characteristics, such as flood peak, volume, and duration is required. Flood frequency analysis often focuses on flood peak values and provides a limited assessment of flood events. To develop effective flood management and mitigation policies, estimation of the scale of potential disasters, incorporating the effects of social factors and climate conditions, is required along with quantitative measures of flood frequency. The Japanese flood risk index, the flood disaster occurrence probability (FDOP), was established based on both natural and social factors. It represents the expectation of damage in the case of a single flood occurrence, which is estimated by integrating a physical-based approach as a Total Runoff Integrating Pathways (TRIP) model with Gumbel distribution metrics. The resulting equations are used to predict potential flood damage based on gridded Japanese data for independent variables. This approach is novel in that it targets floods based on units of events instead of a long-term trend. Moreover, the FDOP can express relative potential flood risk while considering flood damage. The significance of the present study is that both the hazard parameters (which contribute directly to flood occurrence) and vulnerability parameters (which reflect conditions of the region where the flood occurred), including residential and social characteristics, were shown quantitatively to affect flood damage. This study examined the probability of flood disaster occurrence using the TRIP model for Japan (J-TRIP), a river routing scheme that provides a digital river network covering Japan. The analysis was based on floods from 1976 to 2004 associated with flood inundation and sediment disasters. Based on these results, we estimated the probability of flood damage officially reported for the whole region of Japan at a grid interval of 0.1 degrees. The relationship between the magnitude of the rain hazard expressed as the probability of exceedance and the probability of flood damage officially reported was expressed as an exponential function by equalizing the whole region of Japan based on excess probability. Moreover, the probabilities of flood damage occurrence according to social factors and changes in climate conditions were also examined. The probability of flood damage occurrence is high, especially in regions of high population density. The results also showed the effect of the dam maintenance ratio on extreme flooding and flood damage frequency. The probability of flood damage occurrence was expected to increase during extreme weather events at the end of this century. These findings provide a sound foundation for use in catchment water resources management