The number of automated measuring and reporting systems used in water distribution and sewer systems is dramatically increasing and, as a consequence, so is the volume of data acquired.Since real-time data is likely to contain a certain amount of anomalous values and data acquisition equipment is not perfect, it is essential to equip the SCADA (Supervisory Control and Data Acquisition) system with automatic procedures that can detect the related problems and assist the user in monitoring and managing the incoming data. A number of different anomaly detection techniques and methods exist and can be used with varying success. To improve the performance, these methods must be fine tuned according to crucial aspects of the process monitored and the contexts in which the data are classified. The aim of this paper is to explore if the data context classification and pre-processing techniques can be used to improve the anomaly detection methods, especially in fully automated systems. The methodology developed is tested on sets of real-life data, using different standard and experimental anomaly detection procedures including statistical, model-based and data-mining approaches. The results obtained clearly demonstrate the effectiveness of the suggested anomaly detection methodology.
Advances in sensor technology and the possibility of automated long distance data transmission have made continuous measurements the preferable way of monitoring urban drainage processes. Usually, the collected data have to be processed by an expert in order to detect and mark the wrong data, remove them and replace them with interpolated data. In general, the first step in detecting the wrong, anomaly data is called the data quality assessment or data validation. Data validation consists of three parts: data preparation, validation scores generation and scores interpretation. This paper will present the overall framework for the data quality improvement system, suitable for automatic, semi-automatic or manual operation. The first two steps of the validation process are explained in more detail, using several validation methods on the same set of real-case data from the Belgrade sewer system. The final part of the validation process, which is the scores interpretation, needs to be further investigated on the developed system.
International Cartographic CBOs (International Cartographic Association-ICA) in partnership with the Open Source Geospatial Foundation-OSGeo has started the initiative ICA-OSGeo Labs to promote and use open source technologies in education and research. For many years, the use and development of the open source software and technologies have been present in the field of research and education at the Faculty of Civil Engineering at the Department of Geodesy and Geoinformatics, University of Belgrade. Additionally, at the University of Belgrade, Faculty of Civil Engineering a laboratory called "Laboratory for development of the open source geospatial technologies -OSGL" has recently been established. This paper presents the current experience of the lab members in using the open source software in geoinformatics, research and education as well as the perspectives and future activities of the newly formed laboratory.
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