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P328 - EnviroInfo 2022

https://dl.gi.de/handle/20.500.12116/39388
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Auflistung P328 - EnviroInfo 2022 nach Erscheinungsdatum

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  • Konferenzbeitrag
    Evolution of Disaster Spatial Risk Assessments: a Bibliometric Analysis
    (EnviroInfo 2022, 2022) Wong, Ingrid; Greve, Klaus; Szarzynski, Jörg
    The advancements and availability of geospatial technology and data, combined with a growing understanding of the importance of disaster management and disaster risk reduction, lead to an increase in quantity and quality of publications in spatial disaster risk assessments. This paper presents an overview of an in-depth bibliometric study of the global evolution of such spatial disaster risk assessments from 2000 to 2021. The study revealed an upwards trend in scientific production, with hydrological hazards dominating the research field. However, climate change may further drive research for meteorological and climatological hazards. No direct influence was found between major disasters and the number of assessments. The study also shed light on the conceptual frameworks that guided spatial risk assessments, with hazard and vulnerability being the essential components.
  • Konferenzbeitrag
    Development of a framework for decision support in the context of climate adaptation
    (EnviroInfo 2022, 2022) Wetzel, Simeon; Mäs, Stephan
    Implementing effective adaptation data that enable this knowledge transfer. For this purpose, this article presents a framework that shall simplify the development of these tools and the measures to climate change impacts concerns policymakers worldwide. At the local scale, there is a gap between scientific findings and a translation of these into concrete measures. It requires a network of tools and access to climate data. The underlying data management concept is intended to provide an infrastructure that requires only a few interventions in the operating process for both data suppliers and system administrators. Most of the infrastructure components have already been realised in the context of the ongoing research project KlimaKonform. In addition, there is an outlook on future implementations.
  • Konferenzbeitrag
    Transient numerical simulation for optimization of a water-cooled high-performance computing center with dynamic cooling circuit temperatures – Work-in-progress
    (EnviroInfo 2022, 2022) Bayer, Nils; Kerskes, Henner; Stergiaropoulos, Konstantinos
    Due to the high consumption of electrical energy, data center operators are increasingly aiming for a data center that is as energy-optimized and efficient as possible. The cooling system has a crucial role here. In this context, an innovative control strategy with dynamic cooling circuit temperatures will be investigated. For this purpose, a detailed numerical simulation model of an existing water-cooled HPC data center cooling system was developed. The model was validated using real operating data. It includes the complete heat flow from the rack cooling distribution units to the cooling towers and the district cooling supply. The model can correctly simulate the thermal behavior in detail and is the basis for further investigations and optimization in terms of various criteria like energy or cost efficiency. The modeling and validation approach is presented in this paper.
  • Konferenzbeitrag
    Relation Extraction from Environmental Law Text Using Natural Language Understanding
    (EnviroInfo 2022, 2022) Thimm, Heiko; Schneider, Phil
    In the last decades the highly active area of environmental legislation has produced a vast amount of text documents that contain laws and regulations enacted by various types of rule setters. This large body of legal text documents is still growing with an increasing speed. In order to assure compliance with the regulations, today, corporate specialist spend a lot of time with the reviewing and assessment of these documents. It seems that through the use of text processing assistance tools these important corporate environmental compliance management tasks can be completed in less time. To develop corresponding assistance tools has been the broader goal of this work in which initial text processing experiments with a common Natural Language Understanding pipeline are described. The obtained results confirm that in order to extract meaningful relations from text documents of the environmental legislation area, domain-specific processing techniques that are tailored to the specific language and format of legal text are required.
  • Konferenzbeitrag
    OData - Usage of a REST Based API Standard in Web based Environmental Information Systems
    (EnviroInfo 2022, 2022) Hilbring, Désirée; Becker, Kevin; Markus; Emde, Katharina; van der Schaaf, Hylke; Spandl, Horst; Tauber, Martina
    Environmental information in existing custom-build environmental information systems is manifold. Sharing this information via Web APIs for diverse client types to fulfil the needs of ongoing digitisation efforts is still a challenge. This paper analyses the open standard OData (Open Data Protocol) as a possible communication protocol between independent servers and clients. Of interest is also the question, if the protocol is not only capable of sharing environmental data between independent systems but also if the information provided via OData is sufficient for directly creating a web-based end user client. The developed prototypical implementation is tested in two environmental applications from different domains: a small data overview for decision makers and the integration of information in an environmental platform.
  • Konferenzbeitrag
    Cloud-based Processing of data from Non-Target-Analysis for Tracking Micropollutants in Surface Water
    (EnviroInfo 2022, 2022) Pauw, Viktoria; Hayek Mohamad; Shojaei, Elham; Hachinger, Stephan; Müller, Uwe; Bader, Tobias
    Tens of thousands of chemicals used by consumers, agriculture and industry enter the aquatic environment as micropollutants every day. Using targeted analysis we are so far only able to detect a small subset of the chemicals that are present. Therefore so called non-target screening (NTS) using liquid chromatography in combination with high-resolution mass spectrometry (LCHRMS) is increasingly used by labs to perform more comprehensive monitoring. However, a high degree of variance in measurements and processing workflows results in low comparability of data from separate laboratories. On one hand this is caused by differences in processing techniques which are due to stationary laboratory equipment and on the other hand by differing priorities in the detection strategy and evaluation workflow. The K2I project funded by BMBF aims at fostering collaboration between laboratories by providing a joint platform for uploading and processing LCHRMS data. A cloud based datalake and processing pipeline is being developed. A standardized processing workflow can then be executed which is enhanced by data mining tools including machine learning techniques. An indexing and searching software is employed to create a web based access to the processed data for participants.
  • Konferenzbeitrag
    Optimization paper production through digitalization by developing an assistance system for machine operators including quality forecast: a concept
    (EnviroInfo 2022, 2022) Schroth, Moritz; Hake, Felix; Merker, Konstantin; Becher, Alexander; Klaeger, Tilman; Huesmann, Robin; Eichhorn, Detlef; Oehm, Lukas
    Nowadays cross-industry ranging challenges include the reduction of greenhouse gas emission and enabling a circular economy. However, the production of paper from waste paper is still a highly resource intensive task, especially in terms of energy consumption. While paper machines produce a lot of data, we have identified a lack of utilization of it and implement a concept using an operator assistance system and state-of-the-art machine learning techniques, e.g., classification, forecasting and alarm flood handling algorithms, to support daily operator tasks. Our main objective is to provide situation-specific knowledge to machine operators utilizing available data. We expect this will result in better adjusted parameters and therefore a lower footprint of the paper machines. emission and enabling a circular economy. However, the production of paper from waste paper is still a highly resource intensive task, especially in terms of energy consumption. While paper machines produce a lot of data, we have identified a lack of utilization of it and implement a concept using an operator assistance system and state-of-the-art machine learning techniques, e.g., classification, forecasting and alarm flood handling algorithms, to support daily operator tasks. Our main objective is to provide situation-specific knowledge to machine operators utilizing available data. We expect this will result in better adjusted parameters and therefore a lower footprint of the
  • Konferenzbeitrag
    EnviroInfo 2022 - Komplettband
    (EnviroInfo 2022, 2022)
  • Konferenzbeitrag
    Towards extended reality soundwalks as community noise communication tool
    (EnviroInfo 2022, 2022) Petersen, Iwer
    Noise is getting increasing attention as environmental factor. Communication of potential impact to affected citizens is part of a participatory approach to many projects like e.g., in windenergy projects. Sound, however, is hard to grasp by non-specialists looking at commonly used metrics. Noise maps from sound simulations are often used to communicate expected noise levels from certain sources but only provide abstract insights that still can be hard to transfer for nonexperts. Extended reality (XR) in combination with real-time spatial audio allows to explore the impact of future projects in the built environment as a direct experience. While the generation of a visual environment poses no greater challenge by using geospatial data, realistic modelling of a spatial soundscape from multiple point-sound-sources is not trivial. This paper reflects on the challenges to present an audio-visual windfarm in XR, outlines established techniques that have proven useful as well as challenges and opportunities in creating, delivering, and evaluating virtual soundscapes.
  • Konferenzbeitrag
    Detection of snow-coverage on PV-modules with images based on CNN-techniques
    (EnviroInfo 2022, 2022) Hepp, Dennis; Hempelmann, Sebastian; Behrens, Grit; Friedrich, Werner
    The transition from fossil fuels to renewable energy is considered as very meaningful to mitigate climate change. To integrate weather-dependent energies firmly into the power grid, a forecast of the energy yield is very important. This paper is about renewable energy generation by photovoltaic (PV) systems. The yield of PV-systems depends not only on weather conditions, but in wintertime also on the additional factor “snow cover”. The aim of this work is to detect snow cover on photovoltaic plants to support the energy yield forecast. For this purpose, images of a PV-plant with and without snow cover are used for feature extraction and then analyzed by using a convolutional neural network (CNN).