Poster abstract: Water level estimation in urban ultrasonic/passive infrared flash flood sensor networks using supervised learning
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
Distributed Sensing Systems Laboratory (DSS)
MetadataShow full item record
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.
JournalIPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks
Conference/Event name13th IEEE/ACM International Conference on Information Processing in Sensor Networks, IPSN 2014