Abstract
The large amount of debris generated in the aftermath of hurricane and storm events can cause severe financial and logistical burdens to coastal communities. Existing debris estimation models mainly focus on wind-induced debris and produce estimates with errors of nearly 50%, highlighting the importance of developing more comprehensive models that can account for other types of debris while improving accuracy. Therefore, the objective of this study is to develop a probabilistic framework to estimate the presence and amount of waterborne debris following a severe storm using machine learning (ML) techniques as a function of relevant storm and landcover parameters. Machine learning techniques are leveraged to generate debris presence and volume models, employing pre- and post-event aerial and satellite imagery and a debris removal database for Hurricane Ike, respectively. The results show that the ensemble learning algorithms perform the best for both tasks, with a misclassification error of 5.56% for the debris presence predictive model, and a normalized root mean squared error (RMSE) value of 11.98 for the debris volume model, the lowest RMSE of the tested algorithms. Dual-layer ML models are also investigated, incorporating the debris presence as a predictor in the debris volume model. The results show a percent error of 11.29% for the dual-layer model and an approximately 5.4% increase in performance with respect to the model that does not incorporate debris presence. The generated debris volume and presence models will provide useful tools to inform decision-making, evaluate mitigation strategies, facilitate recovery efforts, and improve resource allocation following a storm event.
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Acknowledgments
The authors gratefully acknowledge the support of this research by the National Science Foundation under awards OISE-1545837, CMMI-2002522, and CMMI-2022469. Any opinions, findings, and conclusions, or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the sponsors. The support from Zach Kortum, Justin Raine, Rebeca Molina, and Brandon Dukes, the undergraduate students doing internships in the Padgett Research Group, and who assisted in the data collection and preprocessing efforts of this study, is highly appreciated. In addition, Rice University’s Fondren Library GIS center is acknowledged for their support in identifying the data used in the testbed analysis. Finally, the authors gratefully acknowledge RMS for providing the HWind data used to inform the wind field parameters and Tetra Tech for providing the debris removal database. Maps throughout this paper were created using ArcGIS software by Esri. ArcGIS and ArcMap are the intellectual property of Esri.
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Journal of Waterway, Port, Coastal, and Ocean Engineering
Volume 149 • Issue 5 • September 2023
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© 2023 American Society of Civil Engineers.
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Received: Jun 22, 2022
Accepted: Mar 21, 2023
Published online: Jun 21, 2023
Published in print: Sep 1, 2023
Discussion open until: Nov 21, 2023
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