DOI: https://doi.org/10.14710/geoplanning.12.2.139-158

Deep Learning for Coastal Erosion Assessment: Case Study of Vietnam’s Coastal Regions

*HongGiang Nguyen  -  Hue University, Viet Nam
HuuBang Tran  -  Institute of Architecture, Construction and Transportation, ThuDauMot University, Thủ Dầu Một City 5500, Vietnam, Viet Nam

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Abstract

Vietnam’s coastal erosion has experienced a significant increase cause climate change and anthropogenic factors over the past decade. This study intends to analyze the trends of coastline erosion, identify the factors that drive it, and utilize deep learning algorithms to estimate the erosion risk in the future. The National Centre for Hydro-Meteorological Forecasting of Vietnam, Open Development Mekong, and Landsat 8 OLI/TIRS satellite pictures taken between the years 2016 and 2022 are the sources of data for the study, in the 52 erosion prone locations across Vietnam’s coastlines. The significant environmental factors for the model are the height of tides, waves, storm intensity, soil porosity, high monsoon rainfall, sea level rise, temperature, and coastal geomorphology. A Pearson correlation analysis indicates the strongest correlation between storm intensity, wave height, temperature alongside a strong negative correlation of tidal height with rainfall and coastal slope. Accuracy of the forecast was performed with five models: Recurrent Neural Network (RNN), Long Short-Term Memory Network (LSTM), Bidirectional Long Short-Term Memory Network (BiLSTM), Bidirectional RNN (BiRNN), and Hybrid RNN_LSTM. Among the tested models, the Hybrid RNN_LSTM outperformed others, achieving R² and a correlation coefficient to gain 0.77 and 0.91, respectively, at the same time, the study emphasized monsoon winds, storms intensity, and tidal height as the most impactful parameters. These findings can form the basis for data-driven policy and management strategies to improve coastal resilience. Further research should consider anthropogenic activities and land use changes to expand scope and improve model accuracy in areas experiencing global erosion.

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Keywords: Coastal Erosion, Remote Sensing, Deep Learning, Influence Factors

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Section: Original Research
Language : EN
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