Date of Award
Hoque, Tamjidul; Abdelguerfi, Mahdi
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.
Benkara, Sofiane, "Levee Seepage Identification from Aerial Images using Machine Learning" (2022). University of New Orleans Theses and Dissertations. 3001.
Available for download on Saturday, May 27, 2023