{"id":"W2927539500","doi":"10.1016/j.jhydrol.2019.03.073","title":"A comparative assessment of flood susceptibility modeling using Multi-Criteria Decision-Making Analysis and Machine Learning Methods","year":2019,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":726,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Topographic Wetness Index; Stream power; Flood myth; Hydrology (agriculture); TOPSIS; Environmental science; Receiver operating characteristic; Drainage basin; Geology; Computer science; Machine learning; Digital elevation model; Remote sensing; Cartography; Geotechnical engineering; Operations research; Mathematics; Geomorphology; Geography; Erosion","routes":{"ca_aff":true,"ca_fund":false,"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":0.04446806662655311,"score_gpt":0.4156093836049934,"score_spread":0.3711413169784403,"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."}}