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
APA StyleAmini, H., Monegaglia, F., Shakeri, R., Tubino, M., & Zolezzi, G. (2024). Meanders on the Move: Can AI-Based Solutions Predict Where They Will Be Located? Water, 16(17), 2460. https://doi.org/10.3390/w16172460