{"id":"W2084678032","doi":"10.1016/j.jhydrol.2010.05.040","title":"Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques","year":2010,"lang":"en","type":"article","venue":"Journal of Hydrology","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":357,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Artificial neural network; Principal component analysis; Data pre-processing; Preprocessor; Computer science; Singular spectrum analysis; Time series; Context (archaeology); Series (stratigraphy); Data mining; Benchmark (surveying); Mode (computer interface); Pattern recognition (psychology); Artificial intelligence; Machine learning; Singular value decomposition","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":[0.0004308207,0.0006772914,0.000444588,0.0003672829,0.0001999334,0.0003581347,0.0005227253,0.000334636,0.0007837833],"category_scores_gemma":[0.001867308,0.000293868,0.0004475078,0.0006249538,0.0001604202,0.0005776591,0.000383176,0.0005430016,0.000175823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002890261,"about_ca_system_score_gemma":0.0003771296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004615332,"about_ca_topic_score_gemma":0.004621163,"domain_scores_codex":[0.9998999,0.00002254111,0.000009303727,0.00003005068,0.00002493078,0.00001332575],"domain_scores_gemma":[0.9994721,0.0002795176,0.00006616585,0.00004083492,0.0001237874,0.00001769798],"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":[0.0001857379,0.0001210232,0.002361078,0.00005954839,0.00009638291,0.00006681759,0.00003054782,0.8521864,0.01392368,0.0008658986,0.0005655948,0.1295372],"study_design_scores_gemma":[0.000003206971,0.00001016188,0.0003629891,7.458078e-7,0.000005634737,0.000002153478,7.94243e-7,0.9982404,0.001152575,0.0001841547,0.00003562975,0.000001583295],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.301549,0.0002111868,0.6949593,0.0001237519,0.0001202819,0.00004561393,0.0001498659,0.00170725,0.001133808],"genre_scores_gemma":[0.9056361,0.0001112941,0.09317886,0.00002341815,0.00005124539,0.0000577875,0.0002118081,0.00005101548,0.0006784688],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004615332,"threshold_uncertainty_score":0.00917697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02989246379809274,"score_gpt":0.2506433346021303,"score_spread":0.2207508708040376,"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."}}