{"id":"W3017246359","doi":"10.2166/hydro.2020.095","title":"Deep learning convolutional neural network in rainfall–runoff modelling","year":2020,"lang":"en","type":"article","venue":"Journal of Hydroinformatics","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":247,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Computer science; Convolutional neural network; Surface runoff; Artificial intelligence; Evapotranspiration; Deep learning; Time series; Series (stratigraphy); Machine learning; Water resources; Artificial neural network; Data mining; Geology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006409973,0.0001253301,0.000253508,0.00003398776,0.0001049115,0.00003934197,0.000269801,0.00007008546,0.000330986],"category_scores_gemma":[0.0002303098,0.0001039569,0.0001001424,0.0003118778,0.00009446762,0.0005100672,0.0001348574,0.0006906214,0.0001280204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001193503,"about_ca_system_score_gemma":0.00001565067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007208929,"about_ca_topic_score_gemma":0.000003516683,"domain_scores_codex":[0.9982702,0.00005992124,0.0007965691,0.00007561048,0.0004643594,0.0003333642],"domain_scores_gemma":[0.9991491,0.0001142623,0.0004688471,0.00005930617,0.00001243973,0.0001959992],"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":[0.00003429313,0.00001373831,0.008213329,0.000007987111,0.000006287048,0.00002801564,0.001197876,0.9890962,0.00003667623,0.00002972508,0.0003866082,0.0009492782],"study_design_scores_gemma":[0.0003766147,0.0002181821,0.0007359058,0.00002578291,0.000009523736,0.0001239295,0.00005177785,0.9947847,0.000005181163,0.0005602345,0.002994118,0.0001140474],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.977305,0.00005605581,0.01799355,0.0007622809,0.0001167172,0.00006163608,2.795422e-7,0.00002061975,0.003683896],"genre_scores_gemma":[0.9778121,0.00001679563,0.0209227,0.00105204,0.0001667072,3.924254e-7,0.000001449689,0.000009210095,0.00001857705],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007477423,"threshold_uncertainty_score":0.423924,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02431721850055275,"score_gpt":0.2142705786362157,"score_spread":0.189953360135663,"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."}}