Flooding is one of the most dangerous weather events today. Between 2015−2019, on average, flooding has caused more than 130 deaths every year in the USA alone. The devastating nature of flood necessitates the continuous monitoring of water level in the rivers and streams to detect the incoming flood. In this work, we have designed and implemented an efficient vision-based ensemble solution to continuously detect the water level in the creek. Our solution adapts template matching algorithm to find the region of interest by leveraging edge maps, and combines two parallel approach to identify the water level. While first approach fits a linear regression model in edge map to identify the water line, second approach uses a split sliding window to compute the sum of squared difference in pixel intensities to find the water surface. We evaluated the proposed system on 4306 images collected between 3rd October and 18th December in 2019 with the frequency of 1 image in every 10 minutes. The system exhibited low error rate as it achieved 4.8, 3.1% and 0.92 scores for MAE, MAPE and R2 evaluation metrics, respectively. We believe the proposed solution is very practical as it is pervasive, accurate, doesn't require installation of any additional infrastructure in the water body and can be easily adapted to other locations.
@article{arxiv.2201.06098,
title = {An Edge Map based Ensemble Solution to Detect Water Level in Stream},
author = {Pratool Bharti and Priyanjani Chandra and Michael. E. Papka and David Koop},
journal= {arXiv preprint arXiv:2201.06098},
year = {2022}
}