{"id":"W2063987149","doi":"10.1016/j.cageo.2015.04.007","title":"Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling","year":2015,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Landslides and related hazards","field":"Environmental Science","cited_by":823,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Support vector machine; Random forest; Logistic regression; Landslide; Artificial intelligence; Machine learning; Computer science; Receiver operating characteristic; Linear discriminant analysis; Statistics; Statistical model; False positive rate; Data mining; Scale (ratio); Mathematics; Geology; Cartography; Geography; Geomorphology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04085002647121699,"score_gpt":0.3151756414818519,"score_spread":0.2743256150106349,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}