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The GEORES webgis is a tool to help scientists and end users to visualize results from the GEORES project.
Through an intuitive webgis interface, it allows visualizing interactively synchronized maps (canvas) of the 4 targets hazard maps, all the input predictors layers, the hazard maps obtained through application of the Random Forest supervised classification algorithm, and finally the SHAP values associated with each predictor.
SHAP values explain how much each predictor contributes to a machine learning model's prediction for a specific instance.
Each canvas can be controlled in showing several maps of each category, overlapped and visible through transparency sliders.
Layers
244
Predictors Documentation
Layers status
layers
244
Targets
4.25%
Predictors
83%
Risk Susceptibility maps
4.25%
XAI
8.5%
Site locations
Map Controls
Targets Controls
Predicted Risk Controls
Targets (Categorical, 3 classes)
These maps represent the initial categorical single-risk (low/medium/high) valuations.
Predicted Risk Models
These maps show machine learning model predictions for future risk scenarios.
Predictors Controls
XAI Analysis Controls
Predictors Maps
These maps display the feature maps taken in consideration to create risk models.
XAI Analysis Maps
Explainable AI visualization of machine learning model decision factors. All these are mixtures of predictors/features.
