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Future prediction of coastal recession using convolutional neural network

Coastal recession resulting from sea level rise and wave action is a significant environmental concern, posing challenges for accurate long-term predictions due to the intricate interplay of multiple factors. The study explores the potential of machine learning methods, specifically Convolutional Ne...

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Main Authors:	Khan, Abdul Rehman, Ab Razak, Mohd Shahrizal, Yusuf, Badronnisa, Mohd Shafri, Helmi Zulhaidi, Mohamad, Noorasiah
Format:	Article
Published:	Elsevier 2024
Online Access:	http://psasir.upm.edu.my/id/eprint/106143/ https://linkinghub.elsevier.com/retrieve/pii/S0272771424000544
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spelling	my.upm.eprints.1061432024-10-02T02:34:52Z http://psasir.upm.edu.my/id/eprint/106143/ Future prediction of coastal recession using convolutional neural network Khan, Abdul Rehman Ab Razak, Mohd Shahrizal Yusuf, Badronnisa Mohd Shafri, Helmi Zulhaidi Mohamad, Noorasiah Coastal recession resulting from sea level rise and wave action is a significant environmental concern, posing challenges for accurate long-term predictions due to the intricate interplay of multiple factors. The study explores the potential of machine learning methods, specifically Convolutional Neural Network (CNN), for predicting long-term coastal changes caused by sea level rise and wave conditions using beach profile data. The model was trained and validated using historical data, exhibiting a strong similarity between its predictions and the observed beach profile data with mean absolute and root mean square errors ranging between 0.2 and 1 and an R2 value above 0.93. Furthermore, we compared the long-term coastal recession predictions generated by the CNN model, which were extended until the year 2050, with those derived from the widely used Bruun rule, a conventional benchmark in coastal recession prediction. The CNN model's predictions stood out, demonstrating a greater plausibility as compared to the Bruun rule. Modeled profiles closely align with the observed recession of past data, showcasing the CNN model's adaptability, especially for steeper profiles, as seen at Narrabeen-Collaroy Beach and Teluk Cempedak Beach. The CNN model's prediction for Narrabeen-Collaroy Beach indicated a coastal recession of 0.33 m/year for the projected 36-year period (2015–2050), closely mirroring the observed coastal recession of 0.32 m/year over the preceding 36 years (1979–2014). In contrast, the Bruun rule's predicted that recession is notably lower at 0.09 m/year. Similarly, at Teluk Cempedak, the CNN model's forecast of 0.16 m/year falls below the reported 0.8 m/year but surpasses the Bruun-estimated 0.06 m/year. These results emphasize the potential of machine learning methods to significantly enhance our ability to predict future coastal recession, particularly when contrasted with traditional approaches like the Bruun rule. Elsevier 2024-04 Article PeerReviewed Khan, Abdul Rehman and Ab Razak, Mohd Shahrizal and Yusuf, Badronnisa and Mohd Shafri, Helmi Zulhaidi and Mohamad, Noorasiah (2024) Future prediction of coastal recession using convolutional neural network. Estuarine, Coastal and Shelf Science, 299. art. no. 108667. ISSN 1096-0015 https://linkinghub.elsevier.com/retrieve/pii/S0272771424000544 10.1016/j.ecss.2024.108667
institution	Universiti Putra Malaysia
building	UPM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Putra Malaysia
content_source	UPM Institutional Repository
url_provider	http://psasir.upm.edu.my/
description	Coastal recession resulting from sea level rise and wave action is a significant environmental concern, posing challenges for accurate long-term predictions due to the intricate interplay of multiple factors. The study explores the potential of machine learning methods, specifically Convolutional Neural Network (CNN), for predicting long-term coastal changes caused by sea level rise and wave conditions using beach profile data. The model was trained and validated using historical data, exhibiting a strong similarity between its predictions and the observed beach profile data with mean absolute and root mean square errors ranging between 0.2 and 1 and an R2 value above 0.93. Furthermore, we compared the long-term coastal recession predictions generated by the CNN model, which were extended until the year 2050, with those derived from the widely used Bruun rule, a conventional benchmark in coastal recession prediction. The CNN model's predictions stood out, demonstrating a greater plausibility as compared to the Bruun rule. Modeled profiles closely align with the observed recession of past data, showcasing the CNN model's adaptability, especially for steeper profiles, as seen at Narrabeen-Collaroy Beach and Teluk Cempedak Beach. The CNN model's prediction for Narrabeen-Collaroy Beach indicated a coastal recession of 0.33 m/year for the projected 36-year period (2015–2050), closely mirroring the observed coastal recession of 0.32 m/year over the preceding 36 years (1979–2014). In contrast, the Bruun rule's predicted that recession is notably lower at 0.09 m/year. Similarly, at Teluk Cempedak, the CNN model's forecast of 0.16 m/year falls below the reported 0.8 m/year but surpasses the Bruun-estimated 0.06 m/year. These results emphasize the potential of machine learning methods to significantly enhance our ability to predict future coastal recession, particularly when contrasted with traditional approaches like the Bruun rule.
format	Article
author	Khan, Abdul Rehman Ab Razak, Mohd Shahrizal Yusuf, Badronnisa Mohd Shafri, Helmi Zulhaidi Mohamad, Noorasiah
spellingShingle	Khan, Abdul Rehman Ab Razak, Mohd Shahrizal Yusuf, Badronnisa Mohd Shafri, Helmi Zulhaidi Mohamad, Noorasiah Future prediction of coastal recession using convolutional neural network
author_facet	Khan, Abdul Rehman Ab Razak, Mohd Shahrizal Yusuf, Badronnisa Mohd Shafri, Helmi Zulhaidi Mohamad, Noorasiah
author_sort	Khan, Abdul Rehman
title	Future prediction of coastal recession using convolutional neural network
title_short	Future prediction of coastal recession using convolutional neural network
title_full	Future prediction of coastal recession using convolutional neural network
title_fullStr	Future prediction of coastal recession using convolutional neural network
title_full_unstemmed	Future prediction of coastal recession using convolutional neural network
title_sort	future prediction of coastal recession using convolutional neural network
publisher	Elsevier
publishDate	2024
url	http://psasir.upm.edu.my/id/eprint/106143/ https://linkinghub.elsevier.com/retrieve/pii/S0272771424000544
_version_	1814054600154546176
score	13.211869

Future prediction of coastal recession using convolutional neural network

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