{"id":"W2081670007","doi":"10.1029/2010wr009945","title":"Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal, Canada","year":2011,"lang":"en","type":"article","venue":"Water Resources Research","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":490,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Autoregressive integrated moving average; Artificial neural network; Mean squared error; Wavelet; Autoregressive model; Linear regression; Statistics; Econometrics; Demand forecasting; Moving average; Computer science; Environmental science; Meteorology; Mathematics; Time series; Artificial intelligence; Geography; Operations research","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":[0.001780618,0.0006715114,0.0003840544,0.0009771475,0.0003616363,0.0007628971,0.0008570718,0.0002987936,0.0009872322],"category_scores_gemma":[0.005562632,0.0002214109,0.0003744478,0.001243232,0.0002033322,0.0006898179,0.0004406689,0.0004618679,0.0001464486],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00261619,"about_ca_system_score_gemma":0.003363395,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.5787212,"about_ca_topic_score_gemma":0.5861595,"domain_scores_codex":[0.9993346,0.0002564165,0.0000336404,0.00008280271,0.0002420996,0.00005048506],"domain_scores_gemma":[0.9987062,0.0005716692,0.0001120693,0.00004885683,0.0005113825,0.00004986114],"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":[0.000387101,0.0001780248,0.02745042,0.0001565994,0.0003036421,0.0001093678,0.0001180512,0.6701733,0.003673146,0.002898477,0.001799456,0.2927524],"study_design_scores_gemma":[0.00001129788,0.00001959153,0.004326373,0.000004443996,0.00001789223,0.000004559207,0.00001780362,0.994576,0.0006052065,0.0001503328,0.0002573786,0.000009109969],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6309145,0.001962566,0.354447,0.000762112,0.0001503793,0.0001972829,0.0007347789,0.001009066,0.009822302],"genre_scores_gemma":[0.9114737,0.0007052644,0.08263524,0.0000582,0.00002666326,0.0000814046,0.000513418,0.00007216223,0.004433958],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4212788,"threshold_uncertainty_score":0.8475195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1257209807763414,"score_gpt":0.3559706911852541,"score_spread":0.2302497104089127,"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."}}