How to create non landslide point?
Best insight from top research papers
To create non-landslide points, several methods have been proposed in the literature. One approach is to use a subjective random selection method, which has limitations in terms of accuracy and applicability . Another method is to use unsupervised machine learning (UML) models, such as the Gaussian mixture model (GMM) or K-means, to sample non-landslide datasets . These UML models can recognize non-landslide patterns and provide reliable non-landslide samples for landslide susceptibility mapping (LSM) . Additionally, a method based on Information Value (IV) has been proposed, which selects non-landslides based on historical landslide data and conditioning factors . This method has shown high prediction accuracy and is more practical for landslide prevention and engineering construction . Overall, these methods provide different approaches for creating non-landslide points and improving the accuracy of landslide prediction models.
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| Papers (4) | Insight |
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24 Mar 2011 | The provided paper does not provide information on how to create a non-landslide point. |
| The paper proposes a pattern-based approach using unsupervised machine learning (UML) models, such as the Gaussian mixture model (GMM) and K-means, to sample non-landslide datasets. These non-landslide patterns recognized by the UML models are then learned by the random forest (RF) model. | |
06 Mar 2021 | The paper proposes three methods for creating non-landslide points: random selection, buffer selection, and river channel or slope selection. These methods are used to obtain non-landslides for landslide hazard mapping. |
| The provided paper does not provide information on how to create non-landslide points. The paper is about a non-contact landslide hazard monitoring system and method. |
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