{"id":"W2375764845","doi":"10.1515/jwld-2016-0003","title":"A wavelet-SARIMA-ANN hybrid model for precipitation forecasting","year":2016,"lang":"en","type":"article","venue":"Journal of Water and Land Development","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wavelet; Precipitation; Series (stratigraphy); Wavelet transform; Artificial neural network; Meteorology; Environmental science; Computer science; Mathematics; Artificial intelligence; Geography; Geology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005419509,0.00008208855,0.000127809,0.0000285779,0.0001035553,0.00001741173,0.0000704575,0.00002606554,0.00005083432],"category_scores_gemma":[0.00005956496,0.00003592709,0.0000338105,0.00001663671,0.00003630672,0.0001464,0.00005974421,0.00004224569,0.0000101325],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007270269,"about_ca_system_score_gemma":0.00001346128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001919922,"about_ca_topic_score_gemma":0.000003008098,"domain_scores_codex":[0.9992168,0.00001408176,0.0002952732,0.0001106327,0.0001654782,0.000197754],"domain_scores_gemma":[0.9997076,0.00005137207,0.00009686598,0.00003802505,0.00002292326,0.0000832162],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008309532,0.0002413174,0.07563727,0.00007341446,0.0001444365,0.00006333281,0.01267688,0.0304167,0.03903824,0.00001157369,0.006091093,0.8347748],"study_design_scores_gemma":[0.01020311,0.001780297,0.03448416,0.0006243933,0.00016804,0.001596893,0.00006958029,0.6552878,0.180798,0.03665049,0.07682753,0.001509659],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9135132,0.000009633033,0.08571995,0.0004474049,0.00006280437,0.00007112681,0.000001554157,0.000005691055,0.0001686775],"genre_scores_gemma":[0.9463038,0.000005078361,0.05298628,0.0001162317,0.00003731633,0.000003660065,0.000001485214,0.000006453957,0.0005397187],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8332651,"threshold_uncertainty_score":0.1465064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03717630925865342,"score_gpt":0.2302501785158272,"score_spread":0.1930738692571738,"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."}}