Poster abstract: Water level estimation in urban ultrasonic/passive infrared flash flood sensor networks using supervised learning

Mustafa Mousa, Christian G. Claudel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

This article describes a machine learning approach to water level estimation in a dual ultrasonic/passive infrared urban flood sensor system. We first show that an ultrasonic rangefinder alone is unable to accurately measure the level of water on a road due to thermal effects. Using additional passive infrared sensors, we show that ground temperature and local sensor temperature measurements are sufficient to correct the rangefinder readings and improve the flood detection performance. Since floods occur very rarely, we use a supervised learning approach to estimate the correction to the ultrasonic rangefinder caused by temperature fluctuations. Preliminary data shows that water level can be estimated with an absolute error of less than 2 cm. © 2014 IEEE.
Original languageEnglish (US)
Title of host publicationIPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages277-278
Number of pages2
ISBN (Print)9781479931460
DOIs
StatePublished - Apr 2014

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