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Auflistung nach Autor:in "Rueppel, Uwe"

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  • Konferenzbeitrag
    Integrating Decision Support Applications in Computer Based Emergency Management Processes
    (Environmental Informatics and Systems Research, 2007) Rueppel, Uwe; Wagenknrecht, Armin
    As over the last decades natural disasters are increasing all over the world, Disaster and Emergency Management Systems (DEMS) play an important role to shorten the response time and to improve the supporting measures in case of disaster incidents. Many different DEMS where developed in the last years within national and international research projects. In contrast to those approaches, in this paper a concept for a process-driven emergency management system is presented. With this system emergency processes may be modelled, steered and monitored using a formal process modelling language which also allows dynamical changes. The focus of that paper will be on the integration of decision support applications in the emergency management processes. In integrating these applications, the incident command team using the system may not only be supported by guidance through formally modelled emergency processes but also through the results of the decision support applications, represented as “automatic activities” in the process models.
  • Konferenzbeitrag
    Pretreatment of Environmental Data for Forecasting Purposes
    (Environmental Informatics and Industrial Ecology, 2008) Rueppel, Uwe; Goebel, Peter
    To assess present actions on the environment, it is necessary to estimate its impact in the future. Niels Bohr2 recognized, “prediction is very difficult, especially about the future”. Fortunately, “the future is made of the same stuff as the present” (Simone Weil3). This holds the fundamental possibility to forecast. The present is described with data. To draw the right conclusion about the future, the data need to be significant, correct and complete. This work is part of a project for an active control of groundwater levels. In this project, expected groundwater levels are being prognosticated according to varying infiltration masses using Artificial Neural Networks (ANN). Thus, an adequate infiltration quantity will be identified in order to reach the desired groundwater level. Before the environmental data are suitable for the actual forecast purpose, they need to undergo a wide range of pretreatments. These efforts are being described within this paper. In a first step, substitution methods will be presented to impute missing data. Basically, these methods can be divided into two branches. One category with correlation and kriging methods which use related measuring data sets, i.e. data sets of a nearby measuring station for e.g. groundwater level, temperature, rainfall etc.. The other category that uses only the one regarded data set consists of mere statistical methods that are spline interpolation, time-series forecasts and multiple imputations. In a second step, the completed data sets need to be freed from gross errors. For that reason different test criteria like bound checking, comparisons of spacings and different statistical methods are implemented. Furthermore, the original dynamic and time-variant data sets are compared with computed data sets, generated with time series analysis models. Outliers are indicated if computed values strongly diverge from original values. In doubtful situations the current curve can be compared with a curve of a correlative data set, if available. In a third step, in terms of a complexity reduction, the number of the relevant data that serve as input parameters for the ANN need to be reduced without losing the necessary information to make predictions. This is important because in the present case the number of necessary input parameters is too high in comparison to the number of training sets to train the ANN. Different statistical approaches will be discussed, like moving averages, time-weighted transformations and a method to combine sets of moving averages to reduce the number of input parameters of the ANN with consistent information content.
  • Konferenzbeitrag
    Semiautomatic Verification of Groundwater Measured Data
    (Environmental Informatics and Systems Research, 2007) Rueppel, Uwe; Goebel, Peter
    Groundwater is an unseen yet vital resource. It is the most important resource for potable water supply in Germany. Particularly in the state of Germany Hesse, where drinking water originates to 95.2% from groundwater, there is no substitute (HLUG 2007). However, groundwater does not only play an extraordinarily decisive role in watereconomical regards, but as a substantial component of the hydrological cycle, it must be seen with its great importance for the ecological system. The main objectives of groundwater monitoring are to study the variations and long-term trends in the quantitative and qualitative condition of groundwater. The provided information is intended to serve as basis for assessment of environmental quality goals and norms, to ensure compliance with regulations and to prevent excessive use of the groundwater supplies for a sustainable groundwater management. In order to give a representative picture of the groundwater status in consideration of the complexity of hydro-geological systems and heterogeneous groundwater bodies, the net of measuring points is very dense. In a large scaled catchment area, there will be easily some thousands of these groundwater objects, each of them producing groundwater data. In addition with the trend of data loggers in crucial or hard accessible rough nature environments for an automated retrieval and storage of information from one or more sensors, enormous data pools are the result. Due to various reasons, these data do not always have the required quality in order to accomplish the necessary analyses. Since the data pools are too big a manual check up is not suitable. For these reasons series of measurements are in principle to be automatically examined for plausibility – whereas a skilled worker still has the opportunity to make the final decision to obtain high quality data.