{"id":"W2588810797","doi":"10.1007/s00477-017-1394-z","title":"Multi-step water quality forecasting using a boosting ensemble multi-wavelet extreme learning machine model","year":2017,"lang":"en","type":"article","venue":"Stochastic Environmental Research and Risk Assessment","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":126,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Extreme learning machine; Partial autocorrelation function; Adaptive neuro fuzzy inference system; Autocorrelation; Computer science; Mean squared error; Boosting (machine learning); Artificial intelligence; Wavelet; Discrete wavelet transform; Machine learning; Time series; Wavelet transform; Data mining; Statistics; Mathematics; Algorithm; Artificial neural network; Fuzzy logic; Autoregressive integrated moving average; Fuzzy control system","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":[0.001079582,0.0003915979,0.001115176,0.0003660736,0.0003359969,0.0005760528,0.0009191223,0.0008036554,0.0006370279],"category_scores_gemma":[0.001586038,0.0003147901,0.001007746,0.0005660097,0.0002350867,0.0008510197,0.0005751783,0.000980012,0.0001885425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002594468,"about_ca_system_score_gemma":0.0004725464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002319169,"about_ca_topic_score_gemma":0.001914864,"domain_scores_codex":[0.9997298,0.00007435235,0.00001945436,0.0000582644,0.000081703,0.00003642416],"domain_scores_gemma":[0.9994844,0.0002041128,0.00004837806,0.00006200874,0.0001695297,0.00003151902],"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":[0.00009845906,0.00008976777,0.001584553,0.00002827468,0.0000974603,0.00004909481,0.00002273649,0.9491473,0.003446114,0.001970863,0.0004651118,0.0430003],"study_design_scores_gemma":[0.000001353347,0.000005094442,0.0001044314,5.13758e-7,0.00000373817,0.000002466485,5.550478e-7,0.9996095,0.0001019644,0.0001453871,0.00002372533,0.000001197798],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1595596,0.0003448259,0.8376283,0.0001642294,0.0001531192,0.00002490636,0.00007543288,0.0002729726,0.001776607],"genre_scores_gemma":[0.938629,0.0001649211,0.05992201,0.00004661422,0.00005925687,0.00003416643,0.0001198206,0.00001941012,0.00100483],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002319169,"threshold_uncertainty_score":0.00570941,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2678065094809721,"score_gpt":0.4108759280729035,"score_spread":0.1430694185919313,"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."}}