Meanders on the Move: Can AI-Based Solutions Predict Where They Will Be Located?
Abstract
:1. Introduction
2. Material and Methods
2.1. Planform Evolution Processing
2.2. Artificial Intelligence (AI)
3. Results and Discussion
4. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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MLP | |||||
---|---|---|---|---|---|
Activation function | Alpha | Hidden layer size | Solver | ||
Tanh | 0.0005 | 10 | lbfgs | ||
XGBoost | |||||
Learning rate | Max depth | Objective | Sub sample | Col sample | Min child weight |
0.2 | Uniform | Auto | 40 | 2 | Minkowsiki |
Gradient Boosting Regressor (GBR) | |||||
Min sample split | Min sample leaf | Max variable | Learning rate | Number of estimators | Sub sample |
6 | Uniform | Auto | 0.1 | 150 | Minkowski |
Decision Tree | |||||
Criterion | Max depth | Splitter | ccp_alpha | ||
Friedman_mse | 5 | Best | 0.1 |
Metric | eXtreme Gradient Boosting | MLP Regressor | Gradient Boosting Regressor | Decision Tree Regressor |
---|---|---|---|---|
MAE | 0.0264 | 0.2042 | 0.0424 | 0.0343 |
MSE | 0.0023 | 0.0647 | 0.0054 | 0.0064 |
RMSE | 0.0475 | 0.2545 | 0.0733 | 0.0802 |
R2 | 0.9197 | 0.4171 | 0.9516 | 0.9021 |
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Amini, H.; Monegaglia, F.; Shakeri, R.; Tubino, M.; Zolezzi, G. Meanders on the Move: Can AI-Based Solutions Predict Where They Will Be Located? Water 2024, 16, 2460. https://doi.org/10.3390/w16172460
Amini H, Monegaglia F, Shakeri R, Tubino M, Zolezzi G. Meanders on the Move: Can AI-Based Solutions Predict Where They Will Be Located? Water. 2024; 16(17):2460. https://doi.org/10.3390/w16172460
Chicago/Turabian StyleAmini, Hossein, Federico Monegaglia, Reza Shakeri, Marco Tubino, and Guido Zolezzi. 2024. "Meanders on the Move: Can AI-Based Solutions Predict Where They Will Be Located?" Water 16, no. 17: 2460. https://doi.org/10.3390/w16172460