{"id":"W2895196240","doi":"10.1016/j.scitotenv.2018.10.064","title":"An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines","year":2018,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":778,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Topographic Wetness Index; Flood myth; Drainage density; Multivariate statistics; Watershed; Environmental science; Support vector machine; Hydrology (agriculture); Linear discriminant analysis; Flood forecasting; Regression analysis; Flooding (psychology); Flood mitigation; Statistics; Landslide; Machine learning; Mathematics; Computer science; Geography; Engineering; Geotechnical engineering","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":0.0215221100462335,"score_gpt":0.2709740592561505,"score_spread":0.249451949209917,"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."}}