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Machine learning methods for environmental monitoring and flood protection

Author: Pyayt, A.L. · Mokhov, I.I. · Lang, B. · Krzhizhanovskaya, V.V. · Meijer, R.J.
Type:article
Date:2011
Source:World Academy of Science, Engineering and Technology, june, 78, 118-123
Identifier: 445991
Keywords: Early warning system · Flood protection · Intelligent environmental monitoring · Machine learning · Safety and Security · Defence, Safety and Security · Communication & Information · BIS - Business Information Services · TS - Technical Sciences

Abstract

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.