{"id":"W3130047495","doi":"10.2166/ws.2021.049","title":"Short-term water demand predictions coupling an artificial neural network model and a genetic algorithm","year":2021,"lang":"en","type":"article","venue":"Water Science & Technology Water Supply","topic":"Water resources management and optimization","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Autoregressive integrated moving average; Artificial neural network; Genetic algorithm; Hyperparameter; Mean squared error; Term (time); Computer science; Autoregressive model; Algorithm; Time series; Artificial intelligence; Machine learning; Statistics; 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":[0.0006478525,0.0006759011,0.0005164258,0.0004537623,0.00030952,0.0007849954,0.0006848201,0.0009513746,0.00087352],"category_scores_gemma":[0.001594287,0.0003360134,0.0003916497,0.0005453593,0.0003169508,0.000595093,0.0002553755,0.0004888664,0.0001318914],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00148366,"about_ca_system_score_gemma":0.001180388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09620344,"about_ca_topic_score_gemma":0.05852921,"domain_scores_codex":[0.9998142,0.00006464704,0.00001118799,0.00004718231,0.00003815722,0.00002459104],"domain_scores_gemma":[0.9994799,0.0003189271,0.00005090944,0.00001643434,0.0001196404,0.00001422812],"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":[0.000027746,0.00002108306,0.0007838366,0.000006782657,0.00001379581,0.0000130582,0.000005955048,0.9950101,0.000350381,0.0001220837,0.0000570306,0.003588154],"study_design_scores_gemma":[0.000001511734,0.000006937257,0.000156542,9.293195e-7,0.000002027975,8.915786e-7,0.000001653189,0.9996634,0.0001070741,0.0000401625,0.00001769664,0.000001255492],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8207421,0.0003392356,0.1720394,0.0002869792,0.0000632398,0.00006853868,0.0001801234,0.0004642468,0.005816082],"genre_scores_gemma":[0.9860415,0.00005589659,0.01275135,0.00002111805,0.000006601146,0.00003315192,0.00007643933,0.00001077364,0.001003112],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09620344,"threshold_uncertainty_score":0.191287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009657905613878845,"score_gpt":0.2021325593323982,"score_spread":0.1924746537185194,"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."}}