Exploring machine learning algorithms for accurate water level forecasting in Muda river, Malaysia
Accurate water level prediction for both lake and river is essential for flood warning and freshwater resource management. In this study, three machine learning algorithms: multi-layer perceptron neural network (MLP-NN), long short-term memory neural network (LSTM) and extreme gradient boosting XGBo...
Saved in:
Main Authors: | Adli Zakaria M.N., Ahmed A.N., Abdul Malek M., Birima A.H., Hayet Khan M.M., Sherif M., Elshafie A. |
---|---|
Other Authors: | 58480232100 |
Format: | Article |
Published: |
Elsevier Ltd
2024
|
Subjects: | |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Developing NARX Neural Networks for Accurate Water Level Forecasting
by: Basri H., et al.
Published: (2024) -
Graph convolutional network � Long short term memory neural network- multi layer perceptron- Gaussian progress regression model: A new deep learning model for predicting ozone concertation
by: Ehteram M., et al.
Published: (2024) -
River Water Quality Prediction and Analysis�Deep Learning Predictive Models Approach
by: Rizal N.N.M., et al.
Published: (2024) -
River water quality, 1995-1999
by: Unit Perancang Ekonomi
Published: (2011) -
A study of water quality for Sungai Perlis during high tide and low tide
by: Nur Shahidah, Mohd Nasir
Published: (2013)