{"id":"W2108706807","doi":"10.1155/2012/794061","title":"Standard Precipitation Index Drought Forecasting Using Neural Networks, Wavelet Neural Networks, and Support Vector Regression","year":2012,"lang":"en","type":"article","venue":"Applied Computational Intelligence and Soft Computing","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":187,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Artificial neural network; Support vector machine; Computer science; Mean squared error; Wavelet; Artificial intelligence; Machine learning; Data mining; Regression; Algorithm; Precipitation; Statistics; Meteorology; Mathematics; Geography","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001038473,0.0004982739,0.0004432271,0.000637797,0.0001337161,0.0005330378,0.0004014472,0.0003787038,0.0003510296],"category_scores_gemma":[0.003163095,0.0001738608,0.0004063914,0.0009081915,0.0001388391,0.001001784,0.0002974297,0.0004865143,0.00008776866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004512005,"about_ca_system_score_gemma":0.0004028482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0111158,"about_ca_topic_score_gemma":0.009285955,"domain_scores_codex":[0.9996636,0.0001042162,0.00003247636,0.00005538489,0.0001209077,0.00002333258],"domain_scores_gemma":[0.9994007,0.0002885245,0.00009100027,0.00003577986,0.0001667844,0.00001714707],"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":[0.000199446,0.0001206568,0.01671664,0.0001081544,0.0001754916,0.00006667647,0.00003198214,0.8809458,0.003041631,0.001378588,0.0006651296,0.09654988],"study_design_scores_gemma":[0.000005562773,0.00003555383,0.00313446,0.000004565081,0.00001005289,0.000005470981,0.000007965038,0.9955118,0.0006306214,0.0005009233,0.0001470753,0.000005819128],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8240084,0.001149177,0.1701143,0.0002992635,0.0001067181,0.00005835909,0.0005844793,0.0004321849,0.003247211],"genre_scores_gemma":[0.9721788,0.0005276434,0.02611311,0.00002306112,0.0000230015,0.00002973441,0.0003928815,0.00001189039,0.0006999646],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0111158,"threshold_uncertainty_score":0.02210218,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02762651705106631,"score_gpt":0.268448673925309,"score_spread":0.2408221568742427,"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."}}