{"id":"W4412592632","doi":"10.1016/b978-0-443-33631-7.00002-0","title":"Integration of hydrometric upstream runoff and Optimal Pruned Extreme Learning Machine for real-time flood forecasting","year":2025,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph; University of Ottawa; Université Laval","funders":"","keywords":"Upstream (networking); Flood myth; Surface runoff; Flood forecasting; Extreme learning machine; Hydrology (agriculture); Computer science; Environmental science; Real-time computing; Artificial intelligence; Geology; Geography; Geotechnical engineering; Ecology; Archaeology; Biology; Computer network","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0007004788,0.0004740185,0.0007372037,0.0004636936,0.0002098247,0.0006922293,0.0010404,0.0007485778,0.002306249],"category_scores_gemma":[0.001849566,0.0004448305,0.0005136974,0.0007550835,0.0003175593,0.001097363,0.0008782708,0.001003088,0.0004380475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003199808,"about_ca_system_score_gemma":0.0003960931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002579542,"about_ca_topic_score_gemma":0.004296395,"domain_scores_codex":[0.9997358,0.00006052852,0.00002209669,0.00005627338,0.00009930602,0.00002610133],"domain_scores_gemma":[0.9996518,0.0001627691,0.00002495047,0.00004655307,0.0000992616,0.00001474616],"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":[0.00005784127,0.00007038124,0.0005535338,0.00004121377,0.00006291953,0.00005343365,0.00003571898,0.7591674,0.003262777,0.004341788,0.001071994,0.2312809],"study_design_scores_gemma":[0.000001082992,0.000008130957,0.0001163766,0.000002254133,0.000003682476,0.00000625806,0.000001647285,0.9979209,0.000299714,0.001417169,0.000220745,0.000002020422],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02641194,0.0005102847,0.9693488,0.000118304,0.00009012226,0.00001481048,0.00003181676,0.0006586681,0.002815292],"genre_scores_gemma":[0.6641337,0.0004498957,0.3285617,0.00009252804,0.0001397837,0.0000604203,0.0001887601,0.0001595173,0.006213695],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002579542,"threshold_uncertainty_score":0.007715166,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02676681400634741,"score_gpt":0.2316557012878573,"score_spread":0.2048888872815099,"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."}}