Advanced Machine Learning Techniques for Predicting Nha Trang Shorelines

Abstract

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.

Keywords

• Nha Trang coast
• shoreline prediction
• statistical forecasting model
• machine learning
• EOF
• SARIMA