Performances of Different Machine Learning Algorithms for Predicting Saltwater Intrusion in the Vietnamese Mekong Delta Using Limited Input Data: A Study from Ham Luong River

Yến, Trần Thị Hoàng; Pham, Nhat-Tuan; Pham, Quoc Bao; Pham, Thanh-Luu; Ngo, Xuan Quang; Nguyen, Duy H. N.; Nguyen, P. N.; Veettil, Bijeesh Kozhikkodan

doi:10.1134/s0097807822030198

Cited by 12 publications

(3 citation statements)

References 32 publications

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“…LSTM neural networks have a strong learning and predictive ability for time-series data such as sea surface temperature [24] and saltwater intrusion [34].…”

Section: Bidirectional Long Short-term Memorymentioning

confidence: 99%

Assessment and Prediction of Sea Level and Coastal Wetland Changes in Small Islands Using Remote Sensing and Artificial Intelligence

Raj,

Pasfield-Neofitou

2024

Remote Sensing

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Pacific Island countries are vulnerable to the impacts of climate change, which include the risks of increased ocean temperatures, sea level rise and coastal wetland loss. The destruction of wetlands leads not only to a loss of carbon sequestration but also triggers the release of already sequestered carbon, in turn exacerbating global warming. These climate change effects are interrelated, and small island nations continuously need to develop adaptive and mitigative strategies to deal with them. However, accurate and reliable research is needed to know the extent of the climate change effects with future predictions. Hence, this study develops a new hybrid Convolutional Neural Network (CNN) Multi-Layer Bidirectional Long Short-Term Memory (BiLSTM) deep learning model with Multivariate Variational Mode Decomposition (MVMD) to predict the sea level for study sites in the Solomon Islands and Federated States of Micronesia (FSM). Three other artificial intelligence (AI) models (Random Forest (FR), multilinear regression (MLR) and multi-layer perceptron (MLP) are used to benchmark the CNN-BiLSTM model. In addition to this, remotely sensed satellite Landsat imagery data are also used to assess and predict coastal wetland changes using a Random Forest (RF) classification model in the two small Pacific Island states. The CNN-BiLSTM model was found to provide the most accurate predictions (with a correlation coefficient of >0.99), and similarly a high level of accuracy (>0.98) was achieved using a Random Forest (RF) model to detect wetlands in both study sites. The mean sea levels were found to have risen 6.0 ± 2.1 mm/year in the Solomon Islands and 7.2 ± 2.2 mm/year in the FSM over the past two decades. Coastal wetlands in general were found to have decreased in total area for both study sites. The Solomon Islands recorded a greater decline in coastal wetland between 2009 and 2022.

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“…LSTM neural networks have a strong learning and predictive ability for time-series data such as sea surface temperature [24] and saltwater intrusion [34].…”

Section: Bidirectional Long Short-term Memorymentioning

confidence: 99%

Assessment and Prediction of Sea Level and Coastal Wetland Changes in Small Islands Using Remote Sensing and Artificial Intelligence

Raj,

Pasfield-Neofitou

2024

Remote Sensing

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“…Presently, some scholars have employed LSTM models to predict estuarine salinity [19][20][21] . However, these models still relied on empirical selection when choosing input variables and hyperparameters, often requiring extensive experimentation for adjustments.…”

Section: 、 Introductionmentioning

confidence: 99%

Salinity prediction in Qiantang Estuary based on Improved LSTM model

Zheng,

Sun,

Jiao

et al. 2024

Preprint

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Investigating saltwater intrusion is vital for optimal use of estuarine water resources. Presently, diverse data-driven models, mainly neural network models, have been employed to predict tidal estuarine salinity. However, the high nonlinearity, randomness, and instability of salinity sequences pose challenges for accurate estuarine salinity forecasting. In this paper, a multi-factor salinity prediction model using an enhanced Long Short-Term Memory (LSTM) network was proposed, based on measured data from Cangqian and Qibao stations in the Qiantang Estuary during 2011-2012. To improve prediction accuracy, input variables of the model were determined through Grey Relational Analysis (GRA) combined with estuarine dynamic analysis, and hyperparameters were optimized using a multi-strategy Improved Sparrow Search Algorithm (ISSA). Additionally, the model was applied to forecast salinity under different runoff conditions, analyzing the sensitivity of salinity to upstream discharge. Experimental result shows that compared to other models (BP, GRU, LSTM, SSA-LSTM), the new proposed ISSA-LSTM model has smaller errors and higher prediction accuracy, with NSE improving over 5% and other metrics (MAP, MAPE, RMSE) improving over 10%. Thus, the model provides a practical solution for the rapid and precise prediction of estuarine salinity.

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“…The number of input variables and optimal salinity prediction models need to be decided based on their importance assessments and the application of robust ML algorithms [12]. In Vietnam, some studies have applied the Landsat-8 OLI data in the GEE platform and ML algorithms in predicting the salinity intrusion in the Mekong Delta in recent years [13][14][15][16]. However, these studies have not used the BMA algorithm, evaluated the importance of input variables, or mentioned the number of optimal salinity prediction models.…”

Section: Introductionmentioning

confidence: 99%

Application of the bayesian model averaging algorithm in evaluating and selecting optimal salinity prediction models

Quynh,

Hang,

Hieu

et al. 2023

JSTCE

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Salinity intrusion poses significant challenges to coastal regions worldwide. Reliable salinity prediction models can provide valuable information to mitigate the impact and influence of salinity intrusion. However, their accuracy relies mainly on the selected input variables and used optimal models. This study employs the BayesianModel Averaging (BMA) algorithm to evaluate input variable importance and select the most reliable salinityprediction model. Based on an analysis of observed salinity data and climate data extracted from Landsat 8 OLI in the Google Earth Engine platform, the BMA algorithm identifies the significance of critical variables andoptimal salinity prediction models. Various statistical metrics, including R-squared, Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) from the Random Forest method, were used to verify the performance of these optimal salinity prediction models. These obtained results offer foundational knowledge and valuable insights for future studies in determining appropriate input variables and selecting the best optimalsalinity prediction model.

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Performances of Different Machine Learning Algorithms for Predicting Saltwater Intrusion in the Vietnamese Mekong Delta Using Limited Input Data: A Study from Ham Luong River

Cited by 12 publications

References 32 publications

Assessment and Prediction of Sea Level and Coastal Wetland Changes in Small Islands Using Remote Sensing and Artificial Intelligence

Assessment and Prediction of Sea Level and Coastal Wetland Changes in Small Islands Using Remote Sensing and Artificial Intelligence

Salinity prediction in Qiantang Estuary based on Improved LSTM model

Application of the bayesian model averaging algorithm in evaluating and selecting optimal salinity prediction models

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