Ioannis Athanasiadis | Γιάννης Αθανασιάδης
Environmental Modelling & Software
Filed Under (Conference papers) by I N Athanasiadis on 31-05-2006
In Fifth ECAI Workshop on Binding Environmental Sciences and Artificial Intelligence, 17th European Conference on Artificial Intelligence, Riva del Garda, Italy, 2006.
Abstract Air quality forecasting is one of the core elements of contemporary Urban Air Quality Management and Information Systems. Such systems are usually set up in order to serve environmental legislation needs and are tailored towards decision makers (for atmospheric quality problem abatement) and citizens (for early warning and information provision). The pluralism of forecasting methods that are available does not always lead to forecasting success, as the specific characteristics of each area of interest and the complicated, mostly chaotic relationships between air quality, meteorology, emissions and topography, limit the effectiveness of the methods used. On the other hand, the timescale of air quality problems dictate the usage of relatively `fast’ methods, while the varying quality of input data calls for methods that have a low sensitivity in this factor and a high operational potential. For this reason, it is always interesting to perform a comparative study between various air quality forecasting methods and tools. The present paper describes the comparison work performed between several statistical methods and classification algorithms, on the basis of their performance to identify exceedances in the daily vegetation threshold. A second series of experiments is conducted for forecating of multiple hourly ozone values, where costs are empirically introduced for evaluating forecasting performance. Experiments are conducted on a dataset from the Marousi air quality monitoring station Athens, Greece.
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Filed Under (Conference papers) by I N Athanasiadis on 31-05-2006
In Second Biennial Meeting of the Int’l Environmental Modelling and Software Society: Complexity and Integrated Resources Management, (C. Pahl, S. Schmidt, A. E. Rizzoli and A. Jakeman, ed.), pp. 531-536, Osnabrück, Germany, 2004.
Abstract Changes in the natural environment affect our quality of life. Thus, government, industry, and the public call for integrated environmental management systems capable of suppling all parties with validated, accurate and timely information. The `near real-time’ constraint reveals two critical problems in delivering such tasks: the low quality or absence of data, and the changing conditions over a long period. These problems are common in environmental monitoring networks and although harmless for off-line studies, they may be serious for near real-time systems. In this work, we discuss the problem space of near real-time reporting Environmental Management Systems and present a methodology for applying agent technology this area. The proposed methodology applies powerful tools from the Artificial Intelligence sector, such as software agents and machine learning, and identifies the potential use for solving real-world problems. An experimental agent-based prototype developed for monitoring and assessing air-quality in near real time is presented. A community of software agents is assigned to monitor and validate measurements coming from several sensors, to assess air-quality, and, finally, to fire alarms to appropriate recipients, when needed. The architecture of the developed system, its domain ontology, and typical agent interactions are presented. Finally, the deployment of a real-world test case is demonstrated.
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Filed Under (Conference papers) by I N Athanasiadis on 31-05-2006
In Second International ICSC Symposium on Information Technologies in Environmental Engineering (ITEE 2005), (W. Leal-Filho and others, ed.), pp. 253-267, Magdeburg, Germany, 2005.
Abstract Managing environmental information is a demanding task, which engages serious amounts of efforts by environmental scientists working in public institutes or the industrial sector. In order to reduce the workload required and disengage environmental scientists from trivial tasks, as data transformation and reviewing, we developed an Agent-based Middleware for Environmental Information Management (AMEIM for short). Our approach, presented in this paper, utilizes software agents that undertake environmental data management tasks. Software agents in AMEIM are capable to fuse and preprocess environmental data. Taking under account the requirements of the application domain, AMEIM’s core functionalities, the agent-based architecture and the platform developed in Java are detailed. The AMEIM system is fully customizable and follows an extendable architecture. Also, reasoning capabilities can be incorporated into AMEIM agents for supporting decision-support features.
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Filed Under (Conference papers) by I N Athanasiadis on 31-05-2006
In Knowledge Discovery for Environmental Management, (H. Voss, M. Wachowicz, S. Dzeroski and A. Lanza, ed.), pp. 1-12, Bonn, Germany, 2004.
Abstract In this paper an empirical approach for supporting the decision making process involved in an Environmental Management System (EMS) that monitors air quality and triggers air quality alerts is presented. Data uncertainty problems involved in an air quality monitoring network, as recorded measurement validation and estimation of missing and erroneous values, are addressed through the exploitation of data mining techniques. Exhaustive experiments with real world data, resulted trustworthy predictive models, capable to support the decision-making process. The outstanding performance of the induced predictive models indicate the added value of this approach for supporting the decision making process involved in an EMS.
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Filed Under (Conference papers) by I N Athanasiadis on 31-05-2006
In Managing Environmental Knowledge (Proc. 20th Int’l Conference on Informatics for Environmental Protection: EnviroInfo 2006), (K. Tochtermann and A. Scharl, ed.), pp. 505-508, Graz, Austria, 2006.
Abstract Quantitative data-driven decision support models are challenged by the difficulties in handling dynamic and uncertain features of real-world environmental systems. In addition, conditions for environmental management keep changing with time, demanding periodically updated decision support. These properties can be realized by learning from data, using knowledge discovery techniques. In the present paper, data mining techniques are applied for data analysis and for the construction of forecasting modules towards decision making, on the basis of air quality information for Athens, Greece. A number of data mining algorithms have been applied for the construction of forecasting models concerning maximum per day hourly ozone concentration values, for a total of 15 monitoring sites in Athens, Greece. In order to perform the experiments concerning the forecasting capabilities of the selected algorithms, two sets of ozone limit values were applied: the one resulting from the EU experience and practice, following the relevant legislation, and the other resulting from the detailed analysis and classification of the data. Successful forecasts are up to 95 %, demonstrating a good performance that should be considered for air quality forecasting modules applied at an operational basis.
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Filed Under (Conference papers) by I N Athanasiadis on 31-05-2006
In Second Biennial Meeting of the Int’l Environmental Modelling and Software Society: Complexity and Integrated Resources Management, (C. Pahl, S. Schmidt, A. E. Rizzoli and A. Jakeman, ed.), pp. 643-648, Osnabrück, Germany, 2004.
Abstract Lately there is a transition in water management: policy makers leave aside traditional methods focused on additional-supply policies and focus on water conservation using demand control methods. Water Agencies use water-pricing policies as an instrument for controlling residential water demand. However, design and evaluation of a water-pricing policy is a complex task, as economic, social and political constraints have to be incorporated. In order to support policy makers in their tasks, we developed DAWN, a software tool for evaluating water-pricing policies, implemented as a multi-agent system. DAWN simulates the residential water demand-supply chain and enables the design, creation, modification and execution of different scenarios. Software agents behave as water consumers, while econometric and social models are incorporated into them for estimating future consumptions. Scenarios and models can be parameterized through a friendly graphical user interface and software agents are instantiated at runtime. DAWN’s main advantage is that it supports social interaction between consumers, which is activated using agent communication. Thus, variables affecting water consumption and associated with consumer’s social behavior can be included into DAWN scenarios. In this paper, DAWN’s agent architecture is detailed and agent communication using ontologies is discussed. Focus is given on the econometric and social simulation models used for agent reasoning. Finally, the platform developed is presented along with real-world results of its application at the region of Thessaloniki, Greece.
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Filed Under (Conference papers) by I N Athanasiadis on 31-05-2006
In Concurrent Engineering: The Vision for the Future Generation in Research and Applications, (R. Jardim-Goncalves, J. Cha and A. Steiger-Garcao, ed.), pp. 23-30, Madeira, Portugal, 2003.
Abstract This paper describes the design and deployment of an agent community, which is responsible for monitoring and assessing air quality, based on measurements generated by a meteorological station. Software agents acting as mediators or decision makers deliver validated information to the appropriate destinations. We outline the procedure for creating agent ontologies, agent types, and, finally, for training agents based on historical data volumes. The C4.5 algorithm for decision tree extraction is applied on meteorological and air-pollutant measurements. The decision models extracted are related to the validation of incoming measurements and to the estimation of missing or erroneous measurements. Emphasis is given on the agent training process, which must embed these data-driven decision models on software agents in a simple and effortless way. We developed a prototype system, which demonstrates the advantages of agent-based solutions for intelligent environmental applications.
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Filed Under (Conference papers) by I N Athanasiadis on 31-05-2006
In 2006 IEEE International Conference on Fuzzy Systems, World Congress on Computational Intelligence, pp. 231-236, Vancouver, British Columbia, Canada, 2006.
Abstract Accurate and on-line decision-making is required by decision support systems including those ones used for environmental information management. This paper focuses on air quality assessment and demonstrates the added value of applying data mining techniques in operational decision-making. More specifically, the application of Fuzzy Lattice Reasoning (FLR) classifier is investigated. An enhanced FLR learning algorithm is presented that employs a sigmoid valuation function for introducing tunable non-linearities. The FLR classifier is applied here beyond the unit-hypercube. The FLR with a sigmoid positive valuation function demonstrates an improved performance on a dataset from the region of Valencia, Spain regarding an environmental problem. Descriptive decision making knowledge (i.e. rules) for classification is also induced.
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Filed Under (Conference papers) by I N Athanasiadis on 31-05-2006
In 6th International Conference on Recent Advances in Soft Computing, Canterbury, UK, 2006.
Abstract Decision-makers exploit several channels to acquire up-to-date and reliable information, which they combine with background knowledge to formulate their decisions. In the last years, such information is increasingly acquired from the Internet by a conceptually straightforward process that involves: the identification of relevant information sources, the specification of filters that query the sources regularly and return the relevant documents in a local repository, the extraction of the information pieces of interest and the integration of these information pieces with previously accumulated information for subsequent querying or statistical analysis. In this paper, we present a distributed, service-oriented architecture for decision making systems based on Web services, and intelligent agents. In this way, the information needs of a manager in an Extended Enterprise scheme are satisfied, and knowledge sharing in Extended Enterprises is facilitated.
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Filed Under (Conference papers) by I N Athanasiadis on 31-05-2006
In Nineth Panhellenic Conference in Informatics, (I. Pitas and K. Margaritis, ed.), pp. 346-360, Thessaloniki, Greece, 2003.
Abstract We present an integrated tool for preprocessing and analysis of genetic data through data mining. Our goal is the prediction of the functional behavior of proteins, a critical problem in functional genomics. During the last years, many programming approaches have been developed for the identification of short amino-acid chains, which are included in families of related proteins. These chains are called motifs and they are widely used for the prediction of the protein’s behavior, since the latter is dependent on them. The idea to use data mining techniques stems from the sheer size of the problem. Since every protein consists of a specific number of motifs, some stronger than others, the identification of the properties of a protein requires the examination of immeasurable combinations. The presence or absence of stronger motifs affects the way in which a protein reacts. GenMiner is a preprocessing software tool that can receive data from three major protein databases and transform them in a form suitable for input to the WEKA data mining suite. A decision tree model was created using the derived training set and an efficiency test was conducted. Finally, the model was applied to unknown proteins. Our experiments have shown that the use of the decision tree model for mining protein data is an efficient and easy-to-implement solution, since it possesses a high degree of parameterization and therefore, it can be used in a plethora of cases.
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