{"id":"W4386027236","doi":"10.1080/19942060.2023.2242445","title":"Machine learning models coupled with empirical mode decomposition for simulating monthly and yearly streamflows: a case study of three watersheds in Ontario, Canada","year":2023,"lang":"en","type":"article","venue":"Engineering Applications of Computational Fluid Mechanics","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Korea Environmental Industry and Technology Institute; National Research Foundation of Korea; Ministry of Science and ICT, South Korea; Ministry of Education, India; Ministry of Environment; Chung-Ang University; National Research Foundation","keywords":"Mean squared error; Hilbert–Huang transform; Support vector machine; Streamflow; Backpropagation; Artificial neural network; Computer science; Convolutional neural network; Artificial intelligence; Correlation coefficient; Machine learning; Environmental science; Statistics; Mathematics; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001727459,0.0001016899,0.0001634577,0.00007062629,0.00007046942,0.000007931724,0.00007472822,0.00003273426,0.000005307419],"category_scores_gemma":[0.00001795513,0.00009879961,0.00001490798,0.0002875303,0.00001094068,0.00005926098,0.00005953143,0.0001086065,3.415149e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001721079,"about_ca_system_score_gemma":0.00004560241,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.4913401,"about_ca_topic_score_gemma":0.590386,"domain_scores_codex":[0.9991649,0.00001197873,0.0002644405,0.0002024763,0.0002190935,0.0001371546],"domain_scores_gemma":[0.9995125,0.0002574724,0.00006274453,0.00009094841,0.00002960752,0.00004672135],"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":[0.00001735611,0.00006957305,0.003192526,0.00001295011,0.00001398891,0.000009622659,0.000627299,0.995327,0.0003502105,0.0001390348,0.000001647136,0.0002387929],"study_design_scores_gemma":[0.0004706475,0.0002002944,0.001582187,0.000014439,0.00001540207,0.00001562424,0.0000829095,0.9959618,0.00002324558,0.001534211,0.000005586407,0.00009364668],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6925456,0.000002912333,0.3070199,0.00001451561,0.000005795636,0.0003677364,0.00001087527,0.00002900137,0.000003672937],"genre_scores_gemma":[0.9756324,1.882422e-7,0.02417344,0.000004712982,0.000003616201,0.0001083515,0.00005852707,0.00001500988,0.000003756909],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2830868,"threshold_uncertainty_score":0.5120471,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02202038095788546,"score_gpt":0.2648871094874852,"score_spread":0.2428667285295997,"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."}}