A Computer Vision and AI Based Solution to Determine the Change in Water Level in Stream
Event Type
ACM Student Research Competition: Graduate Poster
ACM Student Research Competition: Undergraduate Poster
Posters
Student Program
TP
XO
TimeThursday, 19 November 20208:30am - 5pm EDT
LocationPoster Module
DescriptionFlooding is one of the most dangerous weather events today. Between 2015-2019 on average, it has caused more than 130 deaths every year in the USA alone. World Health Organization has reported that, between 1998-2017, floods have affected more than 2 billion people worldwide. The devastating nature of flood necessitates the continuous monitoring of water level in the rivers and streams in flood-prone areas to detect the incoming flood. In this study, we have designed and implemented a computer vision and AI-based system that continuously detects the water level in the creek. Our solution employs an effective template matching algorithm on edge map images to find the water level coordinates. Next, a linear regression based model finds a straight line through these coordinates, that represents the water level. We evaluated our algorithms on 200 images across several days and achieved 0.949 R-squared score.
