Date of Award
5-2022
Degree Type
Thesis-Restricted
Degree Name
M.S.
Degree Program
Computer Science
Department
Computer Science
Major Professor
Hoque, Tamjidul; Abdelguerfi, Mahdi
Abstract
Levees protect from natural disasters that can threaten human health, infrastructure, and biological systems by protecting low-lying lands near or below sea level from flooding. However, seepage in those levees undermines their structural integrity, leading to failures. Today the United States has approximately over a hundred thousand miles of levee, many of which are reaching or have surpassed their initial design life. Given the concern, there is a need to develop reliable, rapid, and non-intrusive levee monitoring systems to detect the presence of seepage. This study explores the use of Deep Convolutional Neural Network (DCNN) integrated with Discrete Cosine Transform (DCT) and Thepade’s Sorted Block Truncation Coding (TSBTC) to detect seepage in aerial images. It also compares existing models that achieved good results for the classification of aerial images using decisions trees, Support Vector Machines, and k-means clustering. Our model detected seepage with great accuracy, with fewer resources, and faster speed when compared.
Recommended Citation
Benkara, Sofiane, "Levee Seepage Identification from Aerial Images using Machine Learning" (2022). University of New Orleans Theses and Dissertations. 3001.
https://scholarworks.uno.edu/td/3001
Rights
The University of New Orleans and its agents retain the non-exclusive license to archive and make accessible this dissertation or thesis in whole or in part in all forms of media, now or hereafter known. The author retains all other ownership rights to the copyright of the thesis or dissertation.