{"id":"W2986218094","doi":"10.2166/wcc.2019.321","title":"Flood prediction based on weather parameters using deep learning","year":2019,"lang":"en","type":"article","venue":"Journal of Water and Climate Change","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":160,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Flood myth; Support vector machine; Artificial intelligence; Machine learning; Artificial neural network; Flood forecasting; Computer science; Deep learning; Meteorology; Internet of Things; Environmental science; Geography","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":[0.0002451735,0.000433948,0.0003948403,0.0005790692,0.000177317,0.0004413039,0.0002967454,0.0003902544,0.001306174],"category_scores_gemma":[0.0008890841,0.0002024697,0.0003324049,0.0004854607,0.0001386228,0.0006907001,0.000343435,0.000675964,0.0002497025],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004093719,"about_ca_system_score_gemma":0.000468422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009831847,"about_ca_topic_score_gemma":0.008776428,"domain_scores_codex":[0.9999238,0.00001310558,0.00000649819,0.00001874595,0.00001585807,0.00002194878],"domain_scores_gemma":[0.9996696,0.0001479899,0.0000442079,0.00001993277,0.00009146558,0.00002685462],"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":[0.0002728938,0.0001988974,0.0186382,0.0000492328,0.00007147186,0.0001005144,0.00002780142,0.8908724,0.004312058,0.0005821851,0.001966791,0.08290751],"study_design_scores_gemma":[0.000001890119,0.000005835857,0.0008828389,0.000001704851,0.000002314217,0.000002096737,0.000002560965,0.9985177,0.0003184435,0.0002200562,0.00004266509,0.000002011764],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8367123,0.0006623134,0.1532807,0.0007982376,0.000187102,0.00003944456,0.001247846,0.001429844,0.00564219],"genre_scores_gemma":[0.9947191,0.00009242842,0.003984294,0.00002920341,0.00001777917,0.000007984787,0.000378935,0.0000062411,0.0007638636],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009831847,"threshold_uncertainty_score":0.01954925,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0356374776589438,"score_gpt":0.2342438468991091,"score_spread":0.1986063692401653,"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."}}