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contributor authorMagdalena Asborno
contributor authorJacob Broders
contributor authorKenneth N. Mitchell
contributor authorMichael A. Hartman
contributor authorLauren D. Dunkin
date accessioned2024-04-27T22:34:02Z
date available2024-04-27T22:34:02Z
date issued2024/03/01
identifier other10.1061-JWPED5.WWENG-2009.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4296959
description abstractConnecting the Mississippi River and the Gulf of Mexico, the Southwest Pass (SWP) is one of the most highly utilized commercial waterways in the United States. Hard-to-predict accumulation of sediments in the SWP affects the access of deep-draft vessels to four of the nation’s top 15 ports measured by tonnage. The U.S. Army Corps of Engineers (USACE) spends approximately 100 Million USD annually on dredging operations to maintain SWP at a 14.2-meter (50-ft.) depth. Presently, USACE project managers rely on rules-of-thumb with seasonal river stage trends and thresholds to get 10–14 days of lead time for shoaling conditions at the SWP. This work presents the development of a machine learning modeling framework to increase lead times and accuracy of shoaling forecasts in the SWP. Within a multivariate multistep timeseries forecasting framework, several regression models, input variables, and forecasting days are explored. All multivariate machine learning models outperformed an univariate ARIMA model used as baseline. A multilayered perceptron regressor implemented on a 60-day in-lag scenario was found to be the best model to forecast shoaling in the upcoming 45 days. The proposed model may be applied to forecast dredging needs at other critical waterways.
publisherASCE
titleForecasting Sediment Accumulation in the Southwest Pass with Machine-Learning Models
typeJournal Article
journal volume150
journal issue2
journal titleJournal of Waterway, Port, Coastal, and Ocean Engineering
identifier doi10.1061/JWPED5.WWENG-2009
journal fristpage04023022-1
journal lastpage04023022-13
page13
treeJournal of Waterway, Port, Coastal, and Ocean Engineering:;2024:;Volume ( 150 ):;issue: 002
contenttypeFulltext


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