Publication: Sediment load prediction in Johor river: deep learning versus machine learning models
Date
2023
Authors
Latif S.D.
Chong K.L.
Ahmed A.N.
Huang Y.F.
Sherif M.
El-Shafie A.
Journal Title
Journal ISSN
Volume Title
Publisher
Springer Science and Business Media Deutschland GmbH
Abstract
Sediment transport is a normal phenomenon in rivers and streams, contributing significantly to ecosystem production and preservation by replenishing vital nutrients and preserving aquatic life�s natural habitats. Thus, sediment transport prediction through modeling is crucial for predicting flood events, tracking coastal erosion, planning for water supplies, and managing irrigation. The predictability of process-driven models may encounter various restrictions throughout the validation process. Given that data-driven models work on the assumption that the underlying physical process is not requisite, this opens up the avenue for AI-based model as alternative modeling. However, AI-based models, such as ANN and SVM, face problems, such as long-term dependency, which require alternative dynamic procedures. Since their performance as universal function approximation depends on their compatibility with the nature of the problem itself, this study investigated several distinct AI-based models, such as long short-term memory (LSTM), artificial neural network (ANN), and support vector machine (SVM), in predicting sediment transport in the Johor river. The collected historical daily sediment transport data from January 1, 2008, to December 01, 2018, through autocorrelation function, were used as input for the model. The statistical results showed that, despite their ability (deep learning and machine learning) to provide sediment predictions based on historical input datasets, machine learning, such as ANN, might be more prone to overfitting or being trapped in a local optimum than deep learning, evidenced by the worse in all metrics score. With RMSE = 11.395, MAE = 18.094, and R2 = 0.914, LSTM outperformed other models in the comparison. � 2023, The Author(s).
Description
Keywords
Artificial neural network (ANN) , Long short-term memory (LSTM) , Sediment transport prediction , Support vector machine (SVM) , Johor , Johor River , Malaysia , West Malaysia , Brain , Floods , Forecasting , Learning systems , Long short-term memory , Rivers , Sediment transport , Water supply , Artificial neural network , Load predictions , Long short-term memory , Machine learning models , Machine-learning , Neural network and support vector machines , Sediment loads , Sediment transport prediction , Support vector machine , Support vectors machine , artificial neural network , autocorrelation , comparative study , computer simulation , fluvial deposit , machine learning , model validation , numerical model , prediction , sediment transport , support vector machine , Support vector machines