%0 Journal Article
	%A Alexander L. Pyayt and  Ilya I. Mokhov and  Bernhard Lang and  Valeria V. Krzhizhanovskaya and  Robert J. Meijer
	%D 2011
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 54, 2011
	%T Machine Learning Methods for Environmental Monitoring and Flood Protection
	%U https://publications.waset.org/pdf/10345
	%V 54
	%X More and more natural disasters are happening every
year: floods, earthquakes, volcanic eruptions, etc. In order to reduce
the risk of possible damages, governments all around the world are
investing into development of Early Warning Systems (EWS) for
environmental applications. The most important task of the EWS is
identification of the onset of critical situations affecting environment
and population, early enough to inform the authorities and general
public. This paper describes an approach for monitoring of flood
protections systems based on machine learning methods. An
Artificial Intelligence (AI) component has been developed for
detection of abnormal dike behaviour. The AI module has been
integrated into an EWS platform of the UrbanFlood project (EU
Seventh Framework Programme) and validated on real-time
measurements from the sensors installed in a dike.
	%P 549 - 554