Sediment load prediction in Johor river: deep learning versus machine learning models

• Main Authors: Latif S.D., Chong K.L., Ahmed A.N., Huang Y.F., Sherif M., El-Shafie A.
• 其他作者: 57216081524
• 格式: Article
• 出版: Springer Science and Business Media Deutschland GmbH 2024
• 主题: 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; Machine learning models; Machine-learning; Neural network and support vector machines; Sediment loads; 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

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 ev...