Advanced Machine Learning Techniques for Predicting Nha Trang Shorelines

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

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number9476017
Pages (from-to)98132-98149
Number of pages18
JournalIEEE Access
Volume9
DOIs
Publication statusPublished - 2021

Keywords

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

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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