{"id":"W4402174099","doi":"10.2139/ssrn.4945024","title":"Enhancing Flood Forecasting Accuracy Using Prophet: A Comparative Analysis with Arima","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Data Mining and Machine Learning Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Autoregressive integrated moving average; Flood myth; Econometrics; Flood forecasting; Computer science; Statistics; Time series; Meteorology; Geography; Mathematics","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":["metaepi_narrow","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.002086787,0.0004390175,0.0006297681,0.0007282807,0.0005490201,0.001263497,0.001632463,0.0001456679,0.000004625149],"category_scores_gemma":[0.00009269646,0.0003520797,0.000309888,0.001513444,0.00005521309,0.0003126503,0.001452212,0.007738097,0.00002594829],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001057765,"about_ca_system_score_gemma":0.00684908,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004622486,"about_ca_topic_score_gemma":0.00153409,"domain_scores_codex":[0.9958005,0.0001970811,0.0005685485,0.0009347617,0.0005114299,0.001987672],"domain_scores_gemma":[0.9978308,0.0001753566,0.0007493895,0.000868708,0.000238619,0.0001371161],"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":[0.0001077611,0.0004936511,0.001841555,0.0004164688,0.02574365,0.0001707024,0.01913723,0.4452925,0.002154908,0.3722969,0.0001281597,0.1322165],"study_design_scores_gemma":[0.0003126824,0.0002701487,0.00008741018,0.000457778,0.002120764,0.001453655,0.001066376,0.862326,0.0003304532,0.1305616,0.0002799759,0.0007330736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1773274,0.001857801,0.8192335,0.0004428008,0.0001701963,0.0002313076,0.00001078565,0.0001990234,0.0005270824],"genre_scores_gemma":[0.9204304,0.0001227871,0.07870161,0.0000328046,0.0003222945,0.0000346179,0.00003742831,0.00003061294,0.0002874068],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.743103,"threshold_uncertainty_score":0.9998931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03335550437396999,"score_gpt":0.3141038084201405,"score_spread":0.2807483040461705,"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."}}