A Computer Vision and AI Based Solution to Determine the Change in Water Level in Stream

Student: Priyanjani Chandra (Northern Illinois University, Argonne National Laboratory (ANL))
Supervisor: Pratool Bharti (Northern Illinois University)

Abstract: Flooding 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.

ACM-SRC Semi-Finalist: no

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