Summary of Interpretability Of Statistical, Machine Learning, and Deep Learning Models For Landslide Susceptibility Mapping in Three Gorges Reservoir Area, by Cheng Chen et al.
Interpretability of Statistical, Machine Learning, and Deep Learning Models for Landslide Susceptibility Mapping in Three Gorges Reservoir Area
by Cheng Chen, Lei Fan
First submitted to arxiv on: 20 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates the interpretability of statistical, machine learning (ML), and deep learning (DL) models in predicting landslide susceptibility. The authors use various interpretation methods and input factors to compare the performance of different models. The convolutional neural network model achieved the highest accuracy, while Extreme Gradient Boosting and Support Vector Machine also demonstrated strong predictive capabilities. However, the interpretability of predictions varied among different models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study looks at how well machine learning models can predict where landslides might happen. It compares different types of models to see which one works best. The best model was a special kind of neural network called a convolutional neural network. This model is good at predicting when and where landslides will occur. The other models, like Extreme Gradient Boosting and Support Vector Machine, also did well. The study shows that these advanced machine learning models can help us understand what makes landslides more likely to happen. |
Keywords
» Artificial intelligence » Deep learning » Extreme gradient boosting » Machine learning » Neural network » Support vector machine
