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

Handle URI:
http://hdl.handle.net/10754/575820
Title:
Poster abstract: Water level estimation in urban ultrasonic/passive infrared flash flood sensor networks using supervised learning
Authors:
Mousa, Mustafa ( 0000-0001-9355-1343 ) ; Claudel, Christian G. ( 0000-0003-0702-6548 )
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.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Electrical Engineering Program; Distributed Sensing Systems Laboratory (DSS)
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks
Conference/Event name:
13th IEEE/ACM International Conference on Information Processing in Sensor Networks, IPSN 2014
Issue Date:
Apr-2014
DOI:
10.1109/IPSN.2014.6846761
Type:
Conference Paper
ISBN:
9781479931460
Appears in Collections:
Conference Papers; Electrical Engineering Program; Electrical Engineering Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorMousa, Mustafaen
dc.contributor.authorClaudel, Christian G.en
dc.date.accessioned2015-08-24T09:27:04Zen
dc.date.available2015-08-24T09:27:04Zen
dc.date.issued2014-04en
dc.identifier.isbn9781479931460en
dc.identifier.doi10.1109/IPSN.2014.6846761en
dc.identifier.urihttp://hdl.handle.net/10754/575820en
dc.description.abstractThis 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.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.subjectARMAXen
dc.subjectNonlinear Regressionen
dc.subjectWater Level Estimationen
dc.titlePoster abstract: Water level estimation in urban ultrasonic/passive infrared flash flood sensor networks using supervised learningen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentElectrical Engineering Programen
dc.contributor.departmentDistributed Sensing Systems Laboratory (DSS)en
dc.identifier.journalIPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networksen
dc.conference.date15 April 2014 through 17 April 2014en
dc.conference.name13th IEEE/ACM International Conference on Information Processing in Sensor Networks, IPSN 2014en
dc.conference.locationBerlinen
kaust.authorClaudel, Christian G.en
kaust.authorMousa, Mustafaen
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