Publication Date

2020

Document Type

Dissertation/Thesis

First Advisor

Papka, Michael E.

Second Advisor

Bharti, Pratool

Degree Name

M.S. (Master of Science)

Legacy Department

Department of Computer Science

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 thesis, we have designed and implemented a computer vision and AI-based system that continuously detect 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 tested our system on 68,890 images from 96 days and achieved high precision in detecting the water level. We achieved an average of 0.949 R2 score when the algorithm output is compared to the ground truth for 200 images across several days.

Extent

49 pages

Language

eng

Publisher

Northern Illinois University

Rights Statement

In Copyright

Rights Statement 2

NIU theses are protected by copyright. They may be viewed from Huskie Commons for any purpose, but reproduction or distribution in any format is prohibited without the written permission of the authors.

Media Type

Text

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