Advanced Machine Learning Techniques for Predicting Nha Trang Shorelines

Yin, Cheng; Binh, Le T.; Anh, Duong Tran; T., Son; Le, Anh Duc; Nguyen, Van-Hau; Nguyen, Van-Chien; Tinh, Nguyen Xuan; Tanaka, Hitoshi; Viet, Nguyen Trung; Nguyen, Long D.

doi:10.1109/access.2021.3095339

Cited by 12 publications

(3 citation statements)

References 37 publications

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“…The LSTM method also showed better accuracy than the Empirical Orthogonal Function (EOF) method in predicting shoreline change of Nha Trang Coast in South Central Vietnam. Further, the method was also in good agreement compared with Seasonal Auto-regressive Integrated Moving Average (SARIMA) and Neural Network Auto-Regression (NNAR) [6].…”

Section: Introductionmentioning

confidence: 83%

Long Short Term Memory Approach for Shoreline Change Prediction on Eretan Beach

Iryanto,

Satrio,

Ghozali

et al. 2024

jitk

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Eretan Beach is one of the beaches in Indramayu and has a reasonably severe abrasion rate from year to year. The Eretan coastline always experiences significant changes due to erosion every year. Therefore, it is necessary to study changes in the coastline at Eretan beach. This study obtained coastline data from the Google Earth engine using CoastSat, a python-based open-source toolkit, from 1992 – 2022. The open-source geographic information system software used to process the data is the Quantum Geographic Information System. This study aims to analyze the Long Short-term Memory (LSTM) algorithm in predicting shoreline changes at Eretan Beach. The eight optimizer functions in the LSTM are used with nine different scenarios to analyze the algorithm's performance. The results of this study show that RMSProp has the best performance compared to other optimizers. The RMSE and MAPE values on the RMSProp are 35.06258 and 2.2923 on the training data and 9.2457 and 1.06786 on the test data. In addition, from the predictions for the next ten years at transect point 251, it was found that there would be an increase in the coastline.

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Section: Introductionmentioning

confidence: 83%

Long Short Term Memory Approach for Shoreline Change Prediction on Eretan Beach

Iryanto,

Satrio,

Ghozali

et al. 2024

jitk

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show abstract

“…In this paper, the transformer model is compared with a machine learning method SVR and a deep learning method LSTM, which have been proven to be robust coastline prediction methods [31,64].…”

Section: Training Strategies Of Svr Lstm and Transformer Methodsmentioning

confidence: 99%

“…The results showed that the ARIMA and LSTM techniques are both suitable for short-term coastline prediction in Akpakpa. In addition, YIN, ANH, and MAI [31] used NNAR and LSTM methods to predict coastline changes in surveillance camera images, and the LSTM model was employed to overcome the gradient disappearance and explosion problems of recurrent neural networks (RNNs). In conclusion, these models are effective at detecting coastline changes but still lack long-term time-series data learning dependencies for coastline prediction.…”

Section: Introductionmentioning

confidence: 99%

A Transformer Model for Coastline Prediction in Weitou Bay, China

Yang,

Wang,

Feng

et al. 2023

Remote Sensing

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The simulation and prediction of coastline changes are of great significance for the development and scientific management of coastal zones. Coastline changes are difficult to capture completely but appear significantly periodic over a long time series. In this paper, the transformer model is used to learn the changing trend of the coastline so as to deduce the position of the coastline in the coming year. First, we use the distance regularization level set evolution (DRLSE) model for instantaneous waterline extraction (IWE) from preprocessed Landsat time-series images from 2010–2020 in Weitou Bay, China. Then, tidal correction (TC) is performed on the extracted instantaneous waterline dataset to obtain coastlines projected to a single reference tidal datum. Finally, the coastline datasets from 2010–2019 are used for model training, and the coastline in 2020 is used for accuracy assessment. Three precision evaluation methods, including receiver operating characteristic curve matching, the mean offset, and the root mean square error, were used to verify the predicted coastline data. The receiver operating characteristic curve was specifically designed and improved to evaluate the accuracy of the obtained coastline. Compared with the support vector regression (SVR) and long–short-term memory (LSTM) methods, the results showed that the coastline predicted by the transformer model was the closest to the accurate extracted coastline. The accuracies of the correct values corresponding to SVR, LSTM, and transformer models were 88.27%, 94.08%, and 98.80%, respectively, which indicated the accuracy of the coastline extraction results. Additionally, the mean offset and root mean square error were 0.32 pixels and 0.57 pixels, respectively. In addition, the experimental results showed that tidal correction is important for coastline prediction. Moreover, through field investigations of coastlines, the predicted results obtained for natural coastlines were more accurate, while the predicted results were relatively poor for some artificial coastlines that were intensely influenced by human activities. This study shows that the transformer model can provide natural coastline changes for coastal management.

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A Survey on Machine Learning Applications in Renewable Energies Forecasting

Mohabbati

2024

Power Systems

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Advanced Machine Learning Techniques for Predicting Nha Trang Shorelines

Cited by 12 publications

References 37 publications

Long Short Term Memory Approach for Shoreline Change Prediction on Eretan Beach

Long Short Term Memory Approach for Shoreline Change Prediction on Eretan Beach

A Transformer Model for Coastline Prediction in Weitou Bay, China

A Survey on Machine Learning Applications in Renewable Energies Forecasting

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