{"id":"W4378807928","doi":"10.1007/s00477-023-02471-8","title":"Multilayer perceptron-based predictive model using wavelet transform for the reconstruction of missing rainfall data","year":2023,"lang":"en","type":"article","venue":"Stochastic Environmental Research and Risk Assessment","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Korea Meteorological Administration; Chung-Ang University","keywords":"Missing data; Wavelet transform; Multilayer perceptron; Wavelet; Artificial neural network; Mean squared error; Computer science; Statistics; Data mining; Pattern recognition (psychology); Mathematics; Artificial intelligence","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":[0.001618243,0.0004894289,0.001128806,0.0005271361,0.0003157264,0.0007758123,0.001415825,0.0009386945,0.001757287],"category_scores_gemma":[0.003111482,0.0004683843,0.0008667215,0.001015976,0.0003674935,0.001359214,0.000559836,0.001763302,0.0004156817],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004897357,"about_ca_system_score_gemma":0.00100039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0120595,"about_ca_topic_score_gemma":0.007081503,"domain_scores_codex":[0.9996235,0.000111266,0.00003327247,0.00008595443,0.00008363843,0.00006234078],"domain_scores_gemma":[0.999188,0.0005074355,0.00006061345,0.00005164016,0.0001674464,0.0000247545],"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":[0.0001242183,0.00006637683,0.0007918114,0.00005438348,0.00008288483,0.00004716279,0.00002314181,0.9372836,0.001337846,0.003640096,0.0007640606,0.05578434],"study_design_scores_gemma":[0.000001121635,0.00000312621,0.00004293608,9.3788e-7,0.000002890094,0.000001733087,5.875892e-7,0.9995888,0.0000666447,0.000269945,0.0000201607,0.000001177941],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03611419,0.0004584776,0.9617099,0.0001662031,0.00007574528,0.00001539703,0.0001302082,0.0005223783,0.000807419],"genre_scores_gemma":[0.9029351,0.0005938901,0.0920485,0.00009657542,0.00007377149,0.00008588203,0.0004475494,0.00007401573,0.003644741],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0120595,"threshold_uncertainty_score":0.02397865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1306623977355057,"score_gpt":0.3894700761460849,"score_spread":0.2588076784105792,"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."}}