{"doi":"10.20906/cba2024/4309","title":"Predição Conforme para Quantificação de Incertezas no Monitoramento de Estruturas Geotécnicas","abstract":"<jats:p>The monitoring of geotechnical structures generates a large amount of data, and machine learning techniques have been increasingly studied to predict and evaluate this data. However, machine learning-based models generally provide specific predictions without adequately addressing uncertainty. To better understand model uncertainty, the field of UQ has gained attention. In this work, we investigate the use of CP with the estimation of virtual sensors. Our study is divided into two stages: (1) the estimation of virtual sensors using machine learning techniques, and (2) exploring how CP can enhance the understanding of uncertainty in predictive models. We employ walk-forward validation with different time windows to maintain the temporal order of the data and adapt the multiple time series for CP application. The model’s performance is assessed using the Coefficient of Determination (R²), and the quality of the prediction intervals is evaluated through the PICP, PINAW, and CWC metrics. The results indicate that this approach warrants further investigation, as preliminary findings suggest that prediction methodologies can enhance the interpretation and utilization of predictive models.</jats:p>","journal":"Proceedings do Congresso Brasileiro de Automática","year":2024,"id":44365,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":208997,"name":"Eduardo José da Silva Luz","orcid":null,"position":1,"is_corresponding":false},{"id":208995,"name":"Gustavo Pessin","orcid":null,"position":2,"is_corresponding":false},{"id":208996,"name":"Francisco José dos Santos Diniz","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"42279169","pmcid":null,"openalex_id":"https://openalex.org/W4416997139","authors":[],"funders":[{"funder_name":"University of Jordan","grant_id":"49/2023-2024","title":null}],"total_grants":1,"fwci":0.0,"citation_percentile":0.25736434,"influential_citations":0,"citation_trend":[],"oa_status":"bronze","license":null,"oa_locations":[{"url":"https://www.sba.org.br/open_journal_systems/index.php/cba/article/download/4309/3793","host_type":"conference"},{"url":"https://www.sba.org.br/open_journal_systems/index.php/cba/article/download/4309/3793","host_type":"publisher"},{"url":"https://www.sba.org.br/open_journal_systems/index.php/cba/article/view/4309/3793","host_type":"publisher"},{"url":"https://doi.org/10.20906/cba2024/4309","host_type":"conference"}],"fields_of_study":["Seismology and Earthquake Studies","Soil Geostatistics and Mapping","Gaussian Processes and Bayesian Inference","Engineering","Computer Science"],"mesh_terms":[],"keywords":["Field (mathematics)","Predictive modelling","Interpretation (philosophy)","Estimation","Quality (philosophy)","Prediction interval","clinical measurement","limb-length discrepancy","malleolus","radiography"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-06-23T17:45:04.660713Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}