Advanced Machine Learning Techniques for Predicting Nha Trang Shorelines
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 forecas...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9476017/ |
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| author | Cheng Yin Le Thanh Binh Duong Tran Anh Son T. Mai Anh Le Van-Hau Nguyen Van-Chien Nguyen Nguyen Xuan Tinh Hitoshi Tanaka Nguyen Trung Viet Long D. Nguyen Trung Q. Duong |
| author_facet | Cheng Yin Le Thanh Binh Duong Tran Anh Son T. Mai Anh Le Van-Hau Nguyen Van-Chien Nguyen Nguyen Xuan Tinh Hitoshi Tanaka Nguyen Trung Viet Long D. Nguyen Trung Q. Duong |
| author_sort | Cheng Yin |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-627dd55b2e6846eab2f6c2316a2fefee |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-627dd55b2e6846eab2f6c2316a2fefee2025-08-25T23:00:47ZengIEEEIEEE Access2169-35362021-01-019981329814910.1109/ACCESS.2021.30953399476017Advanced Machine Learning Techniques for Predicting Nha Trang ShorelinesCheng Yin0https://orcid.org/0000-0002-9662-3987Le Thanh Binh1Duong Tran Anh2https://orcid.org/0000-0002-6775-0055Son T. Mai3https://orcid.org/0000-0003-4599-1525Anh Le4Van-Hau Nguyen5https://orcid.org/0000-0002-3256-5626Van-Chien Nguyen6https://orcid.org/0000-0003-0005-1855Nguyen Xuan Tinh7https://orcid.org/0000-0001-9712-0583Hitoshi Tanaka8Nguyen Trung Viet9https://orcid.org/0000-0001-5913-2858Long D. Nguyen10https://orcid.org/0000-0002-1044-257XTrung Q. Duong11https://orcid.org/0000-0002-4703-4836Queen’s University Belfast, Belfast, U.K.Vietnam Hydraulic Engineering Consultants Corporation—JSC (HEC), Hanoi, VietnamHo Chi Minh City University of Technology (HUTECH), Ho Chi Minh City, VietnamQueen’s University Belfast, Belfast, U.K.University of Transport, Ho Chi Minh City, VietnamHung Yen University of Technology and Education, Ha’i Du’o’ng, VietnamHanoi University of Science and Technology, Hanoi, VietnamTohoku University, Sendai, JapanTohoku University, Sendai, JapanThuyloi University, Hanoi, VietnamDuy Tan University, Da Nang, VietnamQueen’s University Belfast, Belfast, U.K.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.https://ieeexplore.ieee.org/document/9476017/Nha Trang coastshoreline predictionstatistical forecasting modelmachine learningEOFSARIMA |
| spellingShingle | Cheng Yin Le Thanh Binh Duong Tran Anh Son T. Mai Anh Le Van-Hau Nguyen Van-Chien Nguyen Nguyen Xuan Tinh Hitoshi Tanaka Nguyen Trung Viet Long D. Nguyen Trung Q. Duong Advanced Machine Learning Techniques for Predicting Nha Trang Shorelines IEEE Access Nha Trang coast shoreline prediction statistical forecasting model machine learning EOF SARIMA |
| title | Advanced Machine Learning Techniques for Predicting Nha Trang Shorelines |
| title_full | Advanced Machine Learning Techniques for Predicting Nha Trang Shorelines |
| title_fullStr | Advanced Machine Learning Techniques for Predicting Nha Trang Shorelines |
| title_full_unstemmed | Advanced Machine Learning Techniques for Predicting Nha Trang Shorelines |
| title_short | Advanced Machine Learning Techniques for Predicting Nha Trang Shorelines |
| title_sort | advanced machine learning techniques for predicting nha trang shorelines |
| topic | Nha Trang coast shoreline prediction statistical forecasting model machine learning EOF SARIMA |
| url | https://ieeexplore.ieee.org/document/9476017/ |
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