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Advanced Machine Learning Techniques for Predicting Nha Trang Shorelines

Yin Queen's University Belfast, Belfast, BT7 1NN, United Kingdom|
Trung Q. (14009246700) | Long D. (57191336157); Duong | Nguyen Trung (6602906156); Nguyen | Hitoshi (55546188900); Viet | Nguyen Xuan (55174360400); Tanaka Duy Tan University, Da Nang, 810000, Viet Nam| Van-Chien (57221601676); Tinh Thuyloi University, Hanoi, 10000, Viet Nam| Van-Hau (57680414900); Nguyen Tohoku University, Sendai, 980-8579, Japan| Anh (57226242443); Nguyen Hanoi University of Science and Technology, Hanoi, 10000, Viet Nam| Son T. (51663715300); Le Hung Yen University of Technology and Education, Ha'i Du'o'ng, 17000, Viet Nam| Duong Tran (57202873304); Mai University of Transport, Ho Chi Minh City, 700000, Viet Nam| Le Thanh (57027459700); Anh Ho Chi Minh City University of Technology (HUTECH), Ho Chi Minh City, Viet Nam| Cheng (57192887568); Binh Vietnam Hydraulic Engineering Consultants Corporation - JSC (HEC), Hanoi, Viet Nam|

IEEE Access Số , năm 2021 (Tập 9, trang 98132-98149)

ISSN: 21693536

ISSN: 21693536

DOI: 10.1109/ACCESS.2021.3095339

Tài liệu thuộc danh mục:

Article

English

Từ khóa: Forecasting; Machine learning; Orthogonal functions; Security systems; Video cameras; Auto-regressive integrated moving average; Empirical Orthogonal Function; Extreme weather conditions; Forecasting performance; Machine learning techniques; Short term prediction; Statistical forecasting; Surveillance cameras; Long short-term memory
Tóm tắt tiếng anh
Nha Trang Coast is located in the South Central Vietnam and the coastal erosion has occurred rapidly in recent years. Hence it is crucial to accurately monitor the shoreline changes for better coastal management and reduction of risks for communities. In this paper, we explored a statistical forecasting model, Seasonal Auto-regressive Integrated Moving Average (SARIMA), and two Machine Learning (ML) models, Neural Network Auto-Regression (NNAR) and Long Short-Term Memory (LSTM), to predict the shoreline variations from surveillance camera images. Compared to the Empirical Orthogonal Function (EOF), the most common method used for predicting shoreline changes from cameras, we demonstrate that the SARIMA, NNAR and LSTM models outperform the EOF model significantly in terms of prediction accuracy. The forecasting performance of the SARIMA model, NNAR model and LSTM model is comparable in both long and short-term predictions. The results suggest that these models are highly effective in detecting shoreline changes from video cameras under extreme weather conditions. � 2013 IEEE.

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