Real Time Monitoring of Carbon Monoxide Using Value-at-Risk Measure and Control Charting

One of the most important environmental health issues is air pollution, causing the deterioration of the population’s quality of life, principally in cities where the urbanization level seems limitless. Among ambient pollutants, carbon monoxide (CO) is well known for its biological toxicity. Many studies report associations between exposure to CO and excess mortality. In this context, the present work provides an advanced modelling scheme for real time monitoring of pollution data and especially of carbon monoxide pollution in city level. The real time monitoring is based on an appropriately adjusted multivariate time series model that is used in finance and gives accurate one-step-ahead forecasts. On the output of the time series, we apply an empirical monitoring scheme that is used for the early detection of abnormal increases of CO levels. The proposed methodology is applied in the city of Athens and as the analysis revealed has a valuable performance

Real Time Monitoring of Carbon Monoxide Using Value-at-Risk Measure and Control Charting

One of the most important environmental health issues is air pollution, causing the deterioration of the population’s quality of life, principally in cities where the urbanization level seems limitless. Among ambient pollutants, carbon monoxide (CO) is well known for its biological toxicity. Many studies report associations between exposure to CO and excess mortality. In this context, the present work provides an advanced modelling scheme for real time monitoring of pollution data and especially of carbon monoxide pollution in city level. The real time monitoring is based on an appropriately adjusted multivariate time series model that is used in finance and gives accurate one-step-ahead forecasts. On the output of the time series, we apply an empirical monitoring scheme that is used for the early detection of abnormal increases of CO levels. The proposed methodology is applied in the city of Athens and as the analysis revealed has a valuable performance

Real Time Monitoring of Carbon Monoxide Using Value-at-Risk Measure and Control Charting

One of the most important environmental health issues is air pollution, causing the deterioration of the population’s quality of life, principally in cities where the urbanization level seems limitless. Among ambient pollutants, carbon monoxide (CO) is well known for its biological toxicity. Many studies report associations between exposure to CO and excess mortality. In this context, the present work provides an advanced modelling scheme for real time monitoring of pollution data and especially of carbon monoxide pollution in city level. The real time monitoring is based on an appropriately adjusted multivariate time series model that is used in finance and gives accurate one-step-ahead forecasts. On the output of the time series, we apply an empirical monitoring scheme that is used for the early detection of abnormal increases of CO levels. The proposed methodology is applied in the city of Athens and as the analysis revealed has a valuable performance

Theory of success runs with applications

Στη διατριβή αυτή παρουσιάζεται η μελέτη τυχαίων μεταβλητών που σχετίζονται με προβλήματα ροών επιτυχιών, σε ακολουθίες πειραμάτων με δύο ή περισσότερα αποτελέσματα. Η μελέτη αυτή στηρίζεται στην τεχνική της Μαρκοβιανής εμφύτευσης. Συγκεκριμένα, ορίζεται μια νέα και πολύ γενική κατηγορία διακριτών τυχαίων μεταβλητών των οποίων η κατανομή μπορεί να μελετηθεί με τη χρήση κατάλληλης Μαρκοβιανής αλυσίδας και αναπτύσσονται κατάλληλα εργαλεία για τη μελέτη τυχαίων μεταβλητών που ανήκουν σε αυτή την κατηγορία. Στη συνέχεια μελετώνται μονοδιάστατες και πολυδιάστατες μεταβλητές που ανήκουν σε αυτή την κατηγορία. Τέλος, παρουσιάζονται εφαρμογές των τυχαίων μεταβλητών που μελετήθηκαν σε διάφορους επιστημονικούς τομείς

Controlling Bivariate Categorical Processes using Scan Rules

International audienc

Projecting Annual Rainfall Timeseries Using Machine Learning Techniques

Hydropower plays an essential role in Europe’s energy transition and can serve as an important factor in the stability of the electricity system. This is even more crucial in areas that rely strongly on renewable energy production, for instance, solar and wind power, as for example the Peloponnese and the Ionian islands in Greece. To safeguard hydropower’s contribution to total energy production, an accurate prediction of the annual precipitation is required. Valuable tools to obtain accurate predictions of future observations are firstly a series of sophisticated data preprocessing techniques and secondly the use of advanced machine learning algorithms. In the present paper, a complete procedure is proposed to obtain accurate predictions of meteorological data, such as precipitation. This procedure is applied to the Greek automated weather stations network, operated by the National Observatory of Athens, in the Peloponnese and the Ionian islands in Greece. The proposed prediction algorithm successfully identified the climatic zones based on their different geographic and climatic characteristics for most meteorological stations, resulting in realistic precipitation predictions. For some stations, the algorithm underestimated the annual total precipitation, a weakness also reported by other research works