{"id":"W4412395778","doi":"10.2166/hydro.2025.218","title":"Advancing flood early warning systems: ensemble learning-based classifiers for urban flood forecasting","year":2025,"lang":"en","type":"article","venue":"Journal of Hydroinformatics","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; United Nations University Institute for Water, Environment, and Health; York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Flood myth; Flood forecasting; Warning system; Ensemble learning; Flood warning; Environmental science; Computer science; Machine learning; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002799778,0.0006877905,0.0008584866,0.001202923,0.0003431446,0.001017017,0.0007998515,0.0007081011,0.001073999],"category_scores_gemma":[0.004947253,0.0002542551,0.0005520812,0.0009907972,0.0001696353,0.001363881,0.0009958593,0.001508335,0.0004170869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004416925,"about_ca_system_score_gemma":0.0005327482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004470612,"about_ca_topic_score_gemma":0.004919928,"domain_scores_codex":[0.9992985,0.0002724222,0.00004461984,0.0001034879,0.0002081753,0.00007270584],"domain_scores_gemma":[0.9979637,0.001050639,0.0001246121,0.0001876423,0.0006192783,0.00005433481],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001485746,0.0002000684,0.006506423,0.00005857154,0.000178234,0.0000405548,0.00006521663,0.655075,0.002262855,0.002294025,0.00324017,0.3299302],"study_design_scores_gemma":[0.000003119544,0.00002721869,0.0005670759,0.000009630947,0.00001537588,0.0000050327,0.00000918527,0.9975907,0.0005105819,0.0009375795,0.0003195713,0.000004938589],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1955247,0.002581892,0.7928615,0.00106732,0.0002959316,0.0001155942,0.0004708404,0.001592679,0.005489548],"genre_scores_gemma":[0.9124872,0.000769755,0.08444428,0.0001213705,0.0001693577,0.00007502049,0.0004587667,0.00003819067,0.001436098],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004470612,"threshold_uncertainty_score":0.01480681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008410845418981221,"score_gpt":0.2314972513591789,"score_spread":0.2230864059401977,"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."}}