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

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.

Citation
Mousa, M., & Claudel, C. (2014). Poster abstract: Water level estimation in urban ultrasonic/passive infrared flash flood sensor networks using supervised learning. IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks. doi:10.1109/ipsn.2014.6846761

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

DOI
10.1109/IPSN.2014.6846761

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