Date of Award
Dr. Md Tamjidul Hoque
Dr. Shaikh M. Arifuzzaman
Dr. Minhaz F. Zibran
Dr. Mahdi Abdelguerfi
Levees provide protection for vast amounts of commercial and residential properties. However, these structures degrade over time, due to the impact of severe weather, sand boils, subsidence of land, seepage, etc. In this research, we focus on detecting sand boils. Sand boils occur when water under pressure wells up to the surface through a bed of sand. These make levees especially vulnerable. Object detection is a good approach to confirm the presence of sand boils from satellite or drone imagery, which can be utilized to assist in the automated levee monitoring methodology. Since sand boils have distinct features, applying object detection algorithms to it can result in accurate detection. To the best of our knowledge, this research work is the first approach to detect sand boils from images. In this research, we compare some of the latest deep learning methods, Viola Jones algorithm, and other non-deep learning methods to determine the best performing one. We also train a Stacking-based machine learning method for the accurate prediction of sand boils. The accuracy of our robust model is 95.4%.
Kuchi, Aditi S., "Detection of Sand Boils from Images using Machine Learning Approaches" (2019). University of New Orleans Theses and Dissertations. 2618.
Available for download on Saturday, May 23, 2020