Machine learning applications in detecting sand boils from images
Aditi Kuchi,
Md Tamjidul Hoque,
Mahdi Abdelguerfi,
Maik C. Flanagin
Affiliations
Aditi Kuchi
Department of Computer Science, University of New Orleans, New Orleans, LA, 70148, USA
Md Tamjidul Hoque
Canizaro/Livingston Gulf States Center for Environmental Informatics, University of New Orleans, New Orleans, LA, 70148, USA; Department of Computer Science, University of New Orleans, New Orleans, LA, 70148, USA; Corresponding author. Canizaro/Livingston Gulf States Center for Environmental Informatics, University of New Orleans, New Orleans, LA, 70148, USA.
Mahdi Abdelguerfi
Canizaro/Livingston Gulf States Center for Environmental Informatics, University of New Orleans, New Orleans, LA, 70148, USA; Department of Computer Science, University of New Orleans, New Orleans, LA, 70148, USA
Maik C. Flanagin
US Army Corps of Engineers, New Orleans District, LA, USA
Levees provide protection for vast amounts of commercial and residential properties. However, these structures require constant maintenance and monitoring, due to the threat 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%.
