{"id":"W4316362348","doi":"10.5194/isprs-annals-x-4-w1-2022-677-2023","title":"FLOOD SUSCEPTIBILITY MODELLING USING GEOSPATIAL-BASED MULTI-CRITERIA DECISION MAKING IN LARGE SCALE AREAS","year":2023,"lang":"en","type":"article","venue":"ISPRS annals of the photogrammetry, remote sensing and spatial information sciences","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Flood myth; Geospatial analysis; Topographic Wetness Index; Multiple-criteria decision analysis; Scale (ratio); Environmental science; Damages; Kappa; Natural hazard; Computer science; Cartography; Geography; Operations research; Remote sensing; Digital elevation model; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003655945,0.001125678,0.0009036164,0.002605323,0.0007936871,0.001898193,0.001018661,0.001142125,0.001625489],"category_scores_gemma":[0.004402234,0.0006023136,0.00156482,0.00185551,0.0006297475,0.0009791502,0.001334795,0.0008383855,0.000105654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001853705,"about_ca_system_score_gemma":0.001591172,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0174971,"about_ca_topic_score_gemma":0.01287925,"domain_scores_codex":[0.9985101,0.0007889817,0.0001088237,0.0001955017,0.0002625585,0.0001340828],"domain_scores_gemma":[0.997345,0.001832145,0.0002657165,0.00007620176,0.0003429675,0.0001379278],"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":[0.00004793682,0.0000539433,0.002571927,0.00006164279,0.00006188836,0.000111981,0.00008511355,0.9885803,0.0005724789,0.001090585,0.0001072653,0.006655036],"study_design_scores_gemma":[0.000003621387,0.0000177449,0.0003984839,0.000007505349,0.000007187621,0.000005842235,0.00004362879,0.9984837,0.0001083843,0.00084978,0.00006956764,0.000004656051],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4257532,0.0004130271,0.5684664,0.0004984168,0.00005893766,0.0003905726,0.0004873931,0.0002835095,0.003648523],"genre_scores_gemma":[0.9441518,0.00007525502,0.05507702,0.0000323998,0.00000940072,0.0001424724,0.0001378604,0.000007775624,0.000366055],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0174971,"threshold_uncertainty_score":0.03479052,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06367533041702515,"score_gpt":0.3474098950921999,"score_spread":0.2837345646751748,"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."}}