Poster abstract: Water level estimation in urban ultrasonic/passive infrared flash flood sensor networks using supervised learning
- Resource Type
- Conference
- Authors
- Mousa, Mustafa; Claudel, Christian
- Source
- IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks Information Processing in Sensor Networks, IPSN-14 Proceedings of the 13th International Symposium on. :277-278 Apr, 2014
- Subject
- Communication, Networking and Broadcast Technologies
Signal Processing and Analysis
Temperature measurement
Land surface temperature
Acoustics
Temperature sensors
Estimation
Mathematical model
Wireless sensor networks
Water Level Estimation
ARMAX
Nonlinear Regression
- Language
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.