{"id":"W4281788956","doi":"10.3390/w14111794","title":"The Discharge Forecasting of Multiple Monitoring Station for Humber River by Hybrid LSTM Models","year":2022,"lang":"en","type":"article","venue":"Water","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakes Environmental (Canada); University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Flood myth; Warning system; Flood forecasting; Computer science; Flood warning; Convolutional neural network; Artificial intelligence; Machine learning; Recurrent neural network; Deep learning; Time series; Artificial neural network; Telecommunications; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0002211244,0.0005919827,0.0002477856,0.0004208497,0.0002052205,0.0003258938,0.0005415618,0.0003559025,0.0007261837],"category_scores_gemma":[0.0007492912,0.0001968152,0.0003123662,0.0006283622,0.0001048485,0.0005676924,0.0002922038,0.0004558605,0.000154828],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009226891,"about_ca_system_score_gemma":0.000823195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09547823,"about_ca_topic_score_gemma":0.1223481,"domain_scores_codex":[0.9999063,0.000009418342,0.000006608922,0.00003922484,0.00001893985,0.00001957418],"domain_scores_gemma":[0.99986,0.00004102469,0.00001934453,0.00001239465,0.00005726422,0.000009938965],"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":[0.0003716357,0.000162595,0.02447862,0.0001211046,0.00009721666,0.0003032307,0.0001696338,0.7521154,0.01424706,0.0007291354,0.004965666,0.2022386],"study_design_scores_gemma":[0.000002950242,0.00001400395,0.002513525,0.000002901596,0.000006398966,0.000007344739,0.00001442566,0.9956563,0.00146398,0.0001228885,0.0001911024,0.000004161671],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9215005,0.0005416113,0.07039628,0.0003924957,0.0001322882,0.00003911099,0.002472738,0.001691186,0.00283372],"genre_scores_gemma":[0.988097,0.0001074096,0.009529964,0.00003056961,0.00001307847,0.00001967036,0.0010604,0.00001170034,0.001130246],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09547823,"threshold_uncertainty_score":0.189845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02277106215218343,"score_gpt":0.2285495107460103,"score_spread":0.2057784485938268,"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."}}