Galvin Sim Siang Lin, Hasnah Hashim
Navigating the assessment landscape: Dental students' perceptions of extended-matching questions versus single-correct answers
Journal of dental education
despite success growing datadriven landslide susceptibility prediction model training heavily relies quality data involving topography geology hydrology land cover climate human activity structure model finetuning model parameters datadriven methods considered incorporating landslide priors article prior knowledge statistics related landslide occurrence enhance models perception landslide mechanism main objective contribution study coupling landslide priors deep learning model improve models transferability stability accomplished selecting nonlandslide sample grounded landslide statistics disentangling input landslide features using variational autoencoder crafting loss function physical constraints study utilizes shap method interpret deep learning model aiding acquisition feature permutation results identify underlying landslide causes interpretation result indicates slope influential factor considering extreme rainfall impact landslide occurrences hong kong combine prior deep learning model find feature ranking rainfall improved comparison ranking result interpreted pure mlp potency mtinsar utilized augment landslide susceptibility map promote efficient crossvalidation comparison insar results historical images reveals detectable movement occurrence evident minority landslides landslides occur spontaneously exhibiting precursor motion comparing datadriven methods proposed methods outperform accuracy 2 5 precision 2 7 recall 1 3 f1score 8 10 auroc 2 4 especially cohen kappa performance surpasses nearly 20 indicating knowledgeaware methodology enhances model generalization mitigates training bias induced unbalanced positive negative samples