{"help": "https://pro.iepnb.gob.es/catalogo/api/3/action/help_show?name=package_show", "success": true, "result": {"access_rights": "http://inspire.ec.europa.eu/metadata-codelist/LimitationsOnPublicAccess/noLimitations", "alternate_identifier": "DOI: 10.1016/j.jhydrol.2013.07.009", "applicable_legislation": ["http://data.europa.eu/eli/reg_impl/2023/138/oj"], "author": "Baudron, P., Alonso Sarr\u00eda, F., Garc\u00eda Ar\u00f3stegui, J.L., C\u00e1novas Garc\u00eda, F., Mart\u00ednez Vicente, D. y Moreno Brot\u00f3ns, J.", "author_email": "paul.baudron@baudron.com", "author_name": "Baudron, P., Alonso Sarr\u00eda, F., Garc\u00eda Ar\u00f3stegui, J.L., C\u00e1novas Garc\u00eda, F., Mart\u00ednez Vicente, D. y Moreno Brot\u00f3ns, J.", "classification_variables": {"es": ""}, "conforms_to": ["https://www.boe.es/eli/es/res/2013/02/19/(4)"], "contact_email": "organismo@example.org", "contact_name": "Organismo publicador del Cat\u00e1logo", "contact_role": "http://inspire.ec.europa.eu/metadata-codelist/ResponsiblePartyRole/pointOfContact", "contact_uri": "http://datos.gob.es/recurso/sector-publico/org/Organismo/EA0000000", "contact_url": "https://organismo.example.org/", "created": "2024-11-05", "creator_user_id": "d24a314a-79ce-48c0-abd9-54cb42706da4", "dataset_scope": "non_spatial_dataset", "dcat_type": "http://id.loc.gov/vocabulary/marcgt/art", "encoding": "UTF-8", "featured": false, "graphic_overview": "https://ars.els-cdn.com/content/image/1-s2.0-S0022169413X00228-cov150h.gif", "hvd": "hvd", "hvd_category": ["http://data.europa.eu/bna/c_dd313021"], "id": "9bbcf4b3-b057-5bf7-af20-4729497e8294", "identifier": "9bbcf4b3-b057-5bf7-af20-4729497e8294", "isopen": true, "keyword_iepnb": ["aguas_interiores", "diferenciacion_genetica", "seguimiento", "tecnologia"], "language": "http://publications.europa.eu/resource/authority/language/SPA", "license_id": "cc-by", "license_title": "Creative Commons Attribution", "license_url": "http://www.opendefinition.org/licenses/cc-by", "lineage_source": ["Journal of Hydrology", "vol 499", "303-315"], "maintainer": "", "maintainer_name": "", "metadata_created": "2026-06-23T14:51:50.985969", "metadata_modified": "2026-06-25T12:32:43.377450", "metadata_profile": ["https://semiceu.github.io/DCAT-AP/releases/3.0.0/"], "miteco_data_population": {"es": ""}, "miteco_data_territory": {"es": ""}, "miteco_dataset_type": "http://publications.europa.eu/resource/authority/dataset-type/STATISTICAL", "miteco_geo_level": "1", "modified": "2026-06-25", "name": "9bbcf4b3-b057-5bf7-af20-4729497e8294", "notes": "dentification of the origin of groundwater samples is not always possible in complex multi\u0002layered aquifers. This poses a major difficulty for a reliable interpretation of geochemical results. The problem is especially severe when the information on the tubewells design is hard to obtain. This paper shows a supervised classification method based on the Random Forest (RF) machine learning technique to identify the layer from where groundwater samples were extracted. The classification rules were based on the major ion composition of the samples. We applied this method to the Campo de Cartagena multi-layer aquifer system, in southeastern Spain. A large amount of hydrogeochemical data was avail\u0002able, but only a limited fraction of the sampled tubewells included a reliable determination of the bore\u0002hole design and, consequently, of the aquifer layer being exploited. Added difficulty was the very similar compositions of water samples extracted from different aquifer layers. Moreover, not all groundwater samples included the same geochemical variables. Despite of the difficulty of such a background, the Random Forest classification reached accuracies over 90%. These results were much better than the Lin\u0002ear Discriminant Analysis (LDA) and Decision Trees (CART) supervised classification methods. From a total of 1549 samples, 805 proceeded from one unique identified aquifer, 409 proceeded from a possible blend of waters from several aquifers and 335 were of unknown origin. Only 468 of the 805 unique\u0002aquifer samples included all the chemical variables needed to calibrate and validate the models. Finally, 107 of the groundwater samples of unknown origin could be classified. Most unclassified samples did not feature a complete dataset. The uncertainty on the identification of training samples was taken in account to enhance the model. Most of the samples that could not be identified had an incomplete dataset.", "notes_translated": {"es": "dentification of the origin of groundwater samples is not always possible in complex multi\u0002layered aquifers. This poses a major difficulty for a reliable interpretation of geochemical results. The problem is especially severe when the information on the tubewells design is hard to obtain. This paper shows a supervised classification method based on the Random Forest (RF) machine learning technique to identify the layer from where groundwater samples were extracted. The classification rules were based on the major ion composition of the samples. We applied this method to the Campo de Cartagena multi-layer aquifer system, in southeastern Spain. A large amount of hydrogeochemical data was avail\u0002able, but only a limited fraction of the sampled tubewells included a reliable determination of the bore\u0002hole design and, consequently, of the aquifer layer being exploited. Added difficulty was the very similar compositions of water samples extracted from different aquifer layers. Moreover, not all groundwater samples included the same geochemical variables. Despite of the difficulty of such a background, the Random Forest classification reached accuracies over 90%. These results were much better than the Lin\u0002ear Discriminant Analysis (LDA) and Decision Trees (CART) supervised classification methods. From a total of 1549 samples, 805 proceeded from one unique identified aquifer, 409 proceeded from a possible blend of waters from several aquifers and 335 were of unknown origin. Only 468 of the 805 unique\u0002aquifer samples included all the chemical variables needed to calibrate and validate the models. Finally, 107 of the groundwater samples of unknown origin could be classified. Most unclassified samples did not feature a complete dataset. The uncertainty on the identification of training samples was taken in account to enhance the model. Most of the samples that could not be identified had an incomplete dataset."}, "num_resources": 1, "num_tags": 5, "organization": {"id": "bca483f7-26e2-4ed8-99e8-65f5b3c7a26e", "name": "iepnb", "title": "", "type": "organization", "description": "", "image_url": "", "created": "2026-06-23T14:36:08.942021", "is_organization": true, "approval_status": "approved", "state": "active"}, "owner_org": "bca483f7-26e2-4ed8-99e8-65f5b3c7a26e", "private": false, "provenance": {"es": ""}, "publisher_email": "buzon-bdatos@miteco.es", "publisher_name": "\u00c1rea de Banco de Datos de la Naturaleza. Direcci\u00f3n General Biodiversidad, Bosques y Desertificaci\u00f3n. Ministerio para la Transici\u00f3n Ecol\u00f3gica y el Reto Demogr\u00e1fico", "publisher_type": "http://purl.org/adms/publishertype/NationalAuthority", "publisher_uri": "https://iepnb.es/catalogo/organization/iepnb", "publisher_url": "https://www.miteco.gob.es/", "purpose": {"es": ""}, "reference": [], "schemingdcat_xls_metadata_template": false, "spatial": "{\"type\": \"Polygon\", \"coordinates\": [[[-18.16, 27.64], [4.32, 27.64], [4.32, 43.79], [-18.16, 43.79], [-18.16, 27.64]]]}", "spatial_uri": "http://datos.gob.es/recurso/sector-publico/territorio/Pais/Espa\u00f1a", "state": "active", "study_variables": {"es": ""}, "tag_uri": ["http://inspire.ec.europa.eu/metadata-codelist/TopicCategory/environment"], "thematic_area": ["espacios_protegidos"], "theme_es": ["http://datos.gob.es/kos/sector-publico/sector/medio-ambiente"], "title": "Identifying the origin of groundwater samples in a multi-layer aquifer system with Random Forest classification", "title_translated": {"es": "Identifying the origin of groundwater samples in a multi-layer aquifer system with Random Forest classification"}, "topic": "http://inspire.ec.europa.eu/metadata-codelist/TopicCategory/biota", "type": "dataset", "url": "https://iepnb.es:443/catalogo/dataset/9bbcf4b3-b057-5bf7-af20-4729497e8294", "version_notes": {"es": ""}, "contact": [{"email": "organismo@example.org", "name": "Organismo publicador del Cat\u00e1logo", "role": "http://inspire.ec.europa.eu/metadata-codelist/ResponsiblePartyRole/pointOfContact", "uri": "http://datos.gob.es/recurso/sector-publico/org/Organismo/EA0000000", "url": "https://organismo.example.org/"}], "creator": [{"email": "paul.baudron@baudron.com", "name": "Baudron, P., Alonso Sarr\u00eda, F., Garc\u00eda Ar\u00f3stegui, J.L., C\u00e1novas Garc\u00eda, F., Mart\u00ednez Vicente, D. y Moreno Brot\u00f3ns, J."}], "groups": [{"description": "Grupo de conjuntos de datos del Mar Menor.", "display_name": "Mar Menor", "id": "51cdbdfb-5a8e-46b2-b776-094251cf43d4", "image_display_url": "https://upload.wikimedia.org/wikipedia/commons/c/c0/Isla_del_Bar%C3%B3n_%28Isla_Mayor%29_-_Mar_Menor_-_Cartagena%2C_Murcia_-_Espa%C3%B1a.jpg", "name": "mar_menor", "title": "Mar Menor"}], "publisher": [{"email": "buzon-bdatos@miteco.es", "name": "\u00c1rea de Banco de Datos de la Naturaleza. Direcci\u00f3n General Biodiversidad, Bosques y Desertificaci\u00f3n. Ministerio para la Transici\u00f3n Ecol\u00f3gica y el Reto Demogr\u00e1fico", "type": "http://purl.org/adms/publishertype/NationalAuthority", "uri": "https://iepnb.es/catalogo/organization/iepnb", "url": "https://www.miteco.gob.es/"}], "resources": [{"access_url": "https://iepnb.es:443/catalogo/dataset/9bbcf4b3-b057-5bf7-af20-4729497e8294/resource/1710307a-347a-4906-a876-40d1644002c2", "applicable_legislation": ["http://data.europa.eu/eli/reg_impl/2023/138/oj"], "availability": "http://publications.europa.eu/resource/authority/planned-availability/AVAILABLE", "cache_last_updated": null, "cache_url": null, "created": "2013-07-15T00:00:00", "dataset_id": "f4e6bd0d-9751-4423-89ad-c235e3288fb4", "datastore_active": false, "datastore_contains_all_records_of_source_file": false, "download_url": "https://www.sciencedirect.com/science/article/abs/pii/S002216941300526X", "encoding": "UTF-8", "format": "HTML", "hash": "", "id": "1710307a-347a-4906-a876-40d1644002c2", "language": "http://publications.europa.eu/resource/authority/language/ENG", "last_modified": null, "license": "http://creativecommons.org/licenses/by/4.0/", "license_id": "cc-by", "metadata_modified": "2026-06-25T12:32:43.381915", "mimetype": "https://www.iana.org/assignments/media-types/text/html", "mimetype_inner": null, "modified": "2026-06-25", "name": "Distribuci\u00f3n HTML", "package_id": "9bbcf4b3-b057-5bf7-af20-4729497e8294", "position": 0, "protocol": null, "resource_type": null, "rights": "http://inspire.ec.europa.eu/metadata-codelist/LimitationsOnPublicAccess/noLimitations", "size": 0, "state": "active", "url": "https://www.sciencedirect.com/science/article/abs/pii/S002216941300526X", "url_type": null}], "spatial_coverage": [{"bbox": "{\"type\": \"Polygon\", \"coordinates\": [[[-18.16, 27.64], [4.32, 27.64], [4.32, 43.79], [-18.16, 43.79], [-18.16, 27.64]]]}", "centroid": "{\"type\": \"Point\", \"coordinates\": [-6.92, 35.715]}", "text": "Espa\u00f1a", "uri": "http://datos.gob.es/recurso/sector-publico/territorio/Pais/Espa\u00f1a"}], "tags": [{"display_name": "aguas_interiores", "id": "ddab08b5-a7e4-4187-9b0f-d4459c83a9ba", "name": "aguas_interiores", "state": "active", "vocabulary_id": null}, {"display_name": "diferenciacion_genetica", "id": "b16e0492-80de-4dcd-a301-1403ade94aa4", "name": "diferenciacion_genetica", "state": "active", "vocabulary_id": null}, {"display_name": "marino", "id": "004084d1-68d8-46ca-842a-c10278540bc9", "name": "marino", "state": "active", "vocabulary_id": null}, {"display_name": "seguimiento", "id": "35753cbf-b84a-487f-80a1-3930d85a73a0", "name": "seguimiento", "state": "active", "vocabulary_id": null}, {"display_name": "tecnologia", "id": "0b3381bc-073c-43fe-a133-4efcf0e36300", "name": "tecnologia", "state": "active", "vocabulary_id": null}], "relationships_as_subject": [], "relationships_as_object": []}}