011false100true score desc 2gaptrue5mapcontentxmltrue50object_type_i,object_type_i_lookup,coverage_period_mt,geographic_area_mt,geographic_coordinates_mt,author_role_mt,contributor_role_mt,org_id_mt,org_role_mt,supervisor_mt,supervisor_id_mi,supervisor_id_mi_lookup,fields_of_research_mi,fields_of_research_mi_lookup,display_type_i,display_type_i_lookup,seo_code_mi,seo_code_mi_lookup,copyright_i,license_i,license_i_lookup,oa_compliance_t,oa_notes_t,grant_id_t,funding_body_t,description_of_resource_t,software_required_t,project_description_t,keywords_mt,project_name_t,project_id_t,isdatasetof_mt,isdatasetof_mt_lookup,notes_t,date_dt,xsd_display_option_mi,xsd_display_option_mi_lookup,file_downloads_i,created_date_dt,updated_date_dt,research_program_mt,title_t,depositor_i,isderivationof_mt,assigned_user_id_mt,assigned_group_id_mi,assigned_group_id_mi_lookup,isdatacomponentof_mt,isannotationof_mt,author_id_mi,author_id_mi_lookup,alternative_title_mt,pid_t,publisher_t,author_mt,contributor_mt,contributor_id_mi,contributor_id_mi_lookup,refereed_i,series_t,journal_name_t,newspaper_t,conference_name_t,book_title_t,identifier_mt,edition_t,subject_mi,subject_mi_lookup,place_of_publication_t,start_page_t,end_page_t,chapter_number_t,issue_number_t,volume_number_t,conference_dates_t,conference_location_t,patent_number_t,country_of_issue_t,description_t,date_available_dt,language_mt,phonetic_title_t,language_of_title_mt,translated_title_t,phonetic_journal_name_t,translated_journal_name_t,phonetic_book_title_t,translated_book_title_t,phonetic_newspaper_t,file_attachment_name_mt,translated_newspaper_t,phonetic_conference_name_t,translated_conference_name_t,issn_mt,isbn_mt,isi_loc_t,prn_t,output_availability_t,na_explanation_t,sensitivity_explanation_t,file_attachment_content_mt,org_unit_name_t,org_name_t,report_number_t,sequence_i,genre_t,genre_type_t,formatted_title_t,formatted_abstract_t,parent_publication_t,convener_t,ismemberof_mt,ismemberof_mt_lookup,link_mt,link_description_mt,rights_t,views_i,scopus_id_t,thomson_citation_count_i,gs_citation_count_i,gs_cited_by_link_t,scopus_citation_count_i,status_i,status_i_lookup,first_author_in_document_derived_t,first_author_in_fez_derived_t,ands_collection_type_t,start_date_dt,end_date_dt,access_conditions_t,extent_t,contact_details_email_mt,contact_details_physical_mt,loc_subject_heading_mt,depositor_affiliation_i,surrounding_features_mt,condition_mt,style_mt,period_mt,category_mt,subcategory_mt,structural_systems_mt,adt_id_t,subtype_t,language_of_parent_title_t,proceedings_title_t,file_description_mt,herdc_code_i,herdc_code_i_lookup,herdc_status_i,herdc_status_i_lookup,institutional_status_i,institutional_status_i_lookup,herdc_notes_t,follow_up_flags_i,follow_up_flags_i_lookup,follow_up_flags_imu_i,follow_up_flags_imu_i_lookup,scopus_doc_type_t,scopus_doc_type_t_lookup,wok_doc_type_t,wok_doc_type_t_lookup,conference_id_i,total_chapters_t,publisher_id_i,translated_proceedings_title_t,native_script_title_t,roman_script_title_t,native_script_book_title_t,roman_script_book_title_t,native_script_journal_name_t,roman_script_journal_name_t,native_script_conference_name_t,roman_script_conference_name_t,total_pages_t,native_script_proceedings_title_t,roman_script_proceedings_title_t,language_of_book_title_mt,language_of_journal_name_mt,language_of_proceedings_title_mt,doi_t,author_count_t,collection_year_dt,location_mt,building_materials_mt,architectural_features_mt,interior_features_mt,sherpa_colour_t,ain_detail_t,rj_2010_rank_t,rj_2010_title_t,rj_2012_rank_t,rj_2012_title_t,rc_2010_rank_t,rc_2010_title_t,herdc_code_description_t,score,citation_t1true60 (International Relations AND ismemberof_mt:bibliuned\:master\-ETSInformatica\-IAA AND status_i:(2)) 6display_type_idisplay_type_i_lookup_exactkeywords_mftdate_year_tauthor_id_miauthor_id_mi_lookup_exactauthor_mftjournal_name_t_ftsubject_misubject_mi_lookup_exactgenre_type_t_ftismemberof_mftismemberof_mt_lookup_exactsubtype_t_ftscopus_doc_type_t_ftscopus_doc_type_t_lookup_exact(_authlister_t:(1)) AND (status_i:(2)) 31982016-09-01T00:00:00Z452021-07-19T18:47:01Z2021-07-19T19:21:25ZForecasting Airborne Pollen Concentrations through Random Forestsbibliuned:master-ETSInformatica-IAA-RnavaresPoaceae is the largest family of monocotyledonous flowering plants, known as grasses and considered to be one of the most important aeroallergens in Europe. The increase of allergy cases and the severity of the reactions motivates the prediction of atmospheric concentrations in order to minimize the exposure to risky pollen levels. Also, it is of large interest for clinical institutions in order to apply preventive measures and plan in advance the implications of an increase number of allergy patients. Phenological and meteorological parameters characterize the stages of vegetation development during the growing season. Thus, they can be potentially related to the biological definition of plant phenology. In this thesis, time series of airborne pollen concentrations and meteorological variables measured at the region of Madrid were used to predict risk pollen concentrations for patients. Detailed relationships were established between future airborne concentrations, meteorological data, and flowering states derived from the inner information of the underlying data via computational intelligence models. Therefore, these data were used to develop predictive models for a range of forecast horizons. The proposal will be beneficial to the medical fields related with allergies affections planning and treatment, demonstrating that computational intelligence holds a great potential for aerobiology. In this research, we demonstrated several novel approaches that significantly contribute to the field of aerobiology including: (i) developing a computational intelligencebased model to predict risk concentration levels, and consequently the start and end of pollen season, for long term horizons up to 6 months, on which none of previous works succeed to obtain satisfactory results, (ii) identifying and characterizing the most influential factors which induce the presence of high airborne concentrations for a given set of horizons, using an assumption-free approach which supports biometeorological findings from other authors. The findings of the research are related to producing more accurate prediction models and providing a comprehensive analysis of the relationship of airborne concentrations with various meteorological parameters. As a product of this study, a version of Chapter 3 of this thesis was accepted and presented at the International Work Conference on Time Series Analysis (2016)1, and it has been selected to be extended and submitted as a book chapter in the Springer series Contributions to Statistics2. A version of Chapter 5 has been published as a full paper in the International Journal of Biometeorology3 and Chapter 4 is in preparation to be sent to an international journal for publication.0Doctoral Thesis3182<a class="citation_author_name" title="Navegar por nombre de Autor de Navares Echegaray, Ricardo" href="/fez/list/author/Navares Echegaray, Ricardo/">Navares Echegaray, Ricardo</a>. (<span class="citation_date">2016</span>). <i><a class="citation_title" title="Click para ver : Forecasting Airborne Pollen Concentrations through Random Forests" href="/fez/view/bibliuned:master-ETSInformatica-IAA-Rnavares">Forecasting Airborne Pollen Concentrations through Random Forests</a></i> Master Thesis, <span class="citation_publisher">Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial.</span>Recordmaster TesisPublishedIngeniería InformáticaUniversidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial.Navares Echegaray, RicardoAznarte Mellado, José Luisbibliuned:master-ETSInformatica-IAA-Rnavareshttp://e-spacio.uned.es/fez/view/bibliuned:master-ETSInformatica-IAA-RnavaresengNavares_Echegaray_Ricardo_TFM.pdfpresmd_Navares_Echegaray_Ricardo_TFM.xmlbibliuned:master-ETSInformatica-IAAbibliuned:Settrabajosfindemasterbibliuned:SetopenaireMáster Universitario en I.A. Avanzada: Fundamentos, Métodos y Aplicaciones (UNED)Set de items trabajo fin de másterSet de openaireNavares EchegarayacceptedVersion4.18363231982019-09-25T00:00:00Z13822020-10-19T19:18:01Z2020-10-19T19:18:01ZInterpretable forecasts of NO2 concentrations through deep SHAPbibliuned:master-ETSInformatica-IAA-MvegaIncreasing in pollution levels in cities, especially in developed countries, and the consequences that it has on health and environmental, have prompted institutions to take preventive measures to reduce pollution levels. European Union has established thresholds for certain gases, such as nitrogen dioxide (NO2). If these thresholds are exceeded, institutions of each city belonging to the EU, must activate measures previously defined, to go down NO2 concentrations. Meteorological parameters are related to episodes of high/low NO2 concentrations. In this paper, pollution time series and meteorological features measured in the Madrid region were used to predict NO2 concentrations. Detailed relationships between NO2 concentrations and meteorological data were established through computational intelligence models. Therefore, these data were used to develop a predictive model. The proposal shows good precision in the prediction, proving computational intelligence has great potential in the pollution time series forecasting. When constructing a prediction model, in addition obtaining good accuracy, it is important to know why that prediction is made. Interpreting the output of the computational intelligence model is difficult due to the architecture of the model itself. A SHAP (SHapley Additive Explanations) approach is applied to interpret the complex model outputs. This method assigns each feature a value of importance for a particular prediction. Three SHAP-based explanation methods are compared to determine which method is more suitable for the pollution time series data and for the computational intelligence model chosen. In this research therefore, a model based on computational intelligence is developed to predict the levels of NO2 concentrations. Through methods based on explanations we will obtain a deeper vision of how the computational intelligence model behaves on the pollution time series, allowing an increase in the confidence of the users on the results obtained from the prediction model. As a product of this study, a short version of this document has been sent for consideration in the Ecological Informatics1 international journal on computational ecology and ecological data science.0Doctoral Thesis5312<a class="citation_author_name" title="Navegar por nombre de Autor de Vega García, María" href="/fez/list/author/Vega García, María/">Vega García, María</a>. (<span class="citation_date">2019</span>). <i><a class="citation_title" title="Click para ver : Interpretable forecasts of NO2 concentrations through deep SHAP" href="/fez/view/bibliuned:master-ETSInformatica-IAA-Mvega">Interpretable forecasts of NO2 concentrations through deep SHAP</a></i> Master Thesis, <span class="citation_publisher">Universidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia Artificial</span>Recordmaster TesisPublishedIngeniería InformáticaUniversidad Nacional de Educación a Distancia (España). Escuela Técnica Superior de Ingeniería Informática. Departamento de Inteligencia ArtificialVega García, MaríaAznarte Mellado, Jose Luisbibliuned:master-ETSInformatica-IAA-Mvegahttp://e-spacio.uned.es/fez/view/bibliuned:master-ETSInformatica-IAA-MvegaengVega_Garcia_Maria_TFM.pdfpresmd_Vega_Garcia_Maria_TFM.xmlbibliuned:master-ETSInformatica-IAAbibliuned:Settrabajosfindemasterbibliuned:SetopenaireMáster Universitario en I.A. Avanzada: Fundamentos, Métodos y Aplicaciones (UNED)Set de items trabajo fin de másterSet de openaireVega GarcíaAcceso abierto4.0924797222222222