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dc.contributor.authorMousa, Mustafa
dc.contributor.authorZhang, Xiangliang
dc.contributor.authorClaudel, Christian
dc.date.accessioned2016-08-14T09:14:05Z
dc.date.available2016-08-14T09:14:05Z
dc.date.issued2016-07-19
dc.identifier.citationFlash Flood Detection in Urban Cities Using Ultrasonic and Infrared Sensors 2016:1 IEEE Sensors Journal
dc.identifier.issn1530-437X
dc.identifier.issn1558-1748
dc.identifier.issn2379-9153
dc.identifier.doi10.1109/JSEN.2016.2592359
dc.identifier.urihttp://hdl.handle.net/10754/618396
dc.description.abstractFloods are the most common type of natural disaster. Often leading to loss of lives and properties in the thousands yearly. Among these events, urban flash floods are particularly deadly because of the short timescales on which they occur, and because of the population density of cities. Since most flood casualties are caused by a lack of information on the impending flood (type, location, severity), sensing these events is critical to generate accurate and detailed warnings and short term forecasts. However, no dedicated flash flood sensing systems, that could monitor the propagation of flash floods, in real time, currently exist in cities. In the present paper, firstly a new sensing device that can simultaneously monitor urban flash floods and traffic congestion has been presented. This sensing device is based on the combination of ultrasonic range-finding with remote temperature sensing, and can sense both phenomena with a high degree of accuracy, using a combination of L1-regularized reconstruction and artificial neural networks to process measurement data. Secondly, corresponding algorithms have been implemented on a low-power wireless sensor platform, and their performance in water level estimation in a 6 months test involving four different sensors is illustrated. The results demonstrate that urban water levels can be reliably estimated with error less than 2 cm, and that the preprocessing and machine learning schemes can run in real-time on currently available wireless sensor platforms.
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7516634
dc.rights(c) 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
dc.subjectARMAX
dc.subjectArtificial Neural Networks
dc.subjectFlood Detection
dc.subjectNonlinear Regression
dc.subjectWater Level Estimation
dc.titleFlash Flood Detection in Urban Cities Using Ultrasonic and Infrared Sensors
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering Program
dc.contributor.departmentComputer Science Program
dc.identifier.journalIEEE Sensors Journal
dc.eprint.versionPost-print
dc.contributor.institutionThe University of Texas at Austin
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)
kaust.personMousa, Mustafa
kaust.personZhang, Xiangliang
refterms.dateFOA2018-06-13T12:04:53Z
dc.date.published-online2016-07-19
dc.date.published-print2016-10


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