{"id":"W2965559850","doi":"10.2166/wst.2019.263","title":"Predictive models for wastewater flow forecasting based on time series analysis and artificial neural network","year":2019,"lang":"en","type":"article","venue":"Water Science & Technology","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":84,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hydromantis Environmental Software Solutions (Canada); McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Vienna Science and Technology Fund; Ontario Water Consortium","keywords":"Autoregressive integrated moving average; Inflow; Artificial neural network; Wastewater; Mean squared error; Time series; Multilayer perceptron; Computer science; Engineering; Artificial intelligence; Machine learning; Statistics; Environmental engineering; Meteorology; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0009052913,0.0009871502,0.0008322856,0.000978344,0.0003943098,0.0008963775,0.001033019,0.0009206359,0.001233265],"category_scores_gemma":[0.002440226,0.0003891365,0.0006693824,0.001437634,0.0003069812,0.001056342,0.0004146522,0.00134525,0.000276579],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009303604,"about_ca_system_score_gemma":0.0008060387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02811756,"about_ca_topic_score_gemma":0.01977584,"domain_scores_codex":[0.9996729,0.00008597555,0.00003346131,0.0000827274,0.00009310095,0.00003180383],"domain_scores_gemma":[0.9992366,0.0004797938,0.00009715199,0.00002630106,0.0001470705,0.00001306834],"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":[0.00002835946,0.0000363635,0.0009546819,0.00004094475,0.00004177135,0.00003108271,0.00001977892,0.9744976,0.0005176447,0.001456192,0.0003822038,0.02199328],"study_design_scores_gemma":[7.6865e-7,0.000003098242,0.000105812,0.000001555474,0.000002408696,0.000001721418,0.000001350973,0.9994447,0.0000656822,0.0003090419,0.00006201651,0.000001907905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1047845,0.002220324,0.8842589,0.0005444075,0.0002655782,0.0001057681,0.0006082214,0.00150149,0.005710876],"genre_scores_gemma":[0.9314591,0.001696254,0.06107355,0.00008851028,0.0001152631,0.0002465127,0.000675272,0.00005463918,0.004590855],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02811756,"threshold_uncertainty_score":0.05590779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01383431717718144,"score_gpt":0.2054165585110097,"score_spread":0.1915822413338282,"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."}}