{"id":"https://openalex.org/W2129587808","doi":"https://doi.org/10.1109/ijcnn.2008.4633982","title":"Comparing machine learning methods in estimation of model uncertainty","display_name":"Comparing machine learning methods in estimation of model uncertainty","publication_year":2008,"publication_date":"2008-06-01","ids":{"openalex":"https://openalex.org/W2129587808","doi":"https://doi.org/10.1109/ijcnn.2008.4633982","mag":"2129587808"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2008.4633982","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2008.4633982","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5012707523","display_name":"Durga Lal Shrestha","orcid":"https://orcid.org/0000-0002-5545-1736"},"institutions":[{"id":"https://openalex.org/I2799973717","display_name":"IHE Delft Institute for Water Education","ror":"https://ror.org/030deh410","country_code":"NL","type":"education","lineage":["https://openalex.org/I2799973717"]}],"countries":["NL"],"is_corresponding":true,"raw_author_name":"Durga Lal Shrestha","raw_affiliation_strings":["UNESCO-IHE Institute of Water Education, Delft, Netherlands"],"affiliations":[{"raw_affiliation_string":"UNESCO-IHE Institute of Water Education, Delft, Netherlands","institution_ids":["https://openalex.org/I2799973717"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015835580","display_name":"Dimitri Solomatine","orcid":"https://orcid.org/0000-0003-2031-9871"},"institutions":[{"id":"https://openalex.org/I2799973717","display_name":"IHE Delft Institute for Water Education","ror":"https://ror.org/030deh410","country_code":"NL","type":"education","lineage":["https://openalex.org/I2799973717"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Dimitri P. Solomatine","raw_affiliation_strings":["UNESCO-IHE Institute of Water Education, Delft, Netherlands","UNESCO-IHE Inst. for Water Educ., Delft"],"affiliations":[{"raw_affiliation_string":"UNESCO-IHE Institute of Water Education, Delft, Netherlands","institution_ids":["https://openalex.org/I2799973717"]},{"raw_affiliation_string":"UNESCO-IHE Inst. for Water Educ., Delft","institution_ids":["https://openalex.org/I2799973717"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5012707523"],"corresponding_institution_ids":["https://openalex.org/I2799973717"],"apc_list":null,"apc_paid":null,"fwci":0.4615,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.76036764,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1410","last_page":"1416"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10330","display_name":"Hydrology and Watershed Management Studies","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2312","display_name":"Water Science and Technology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10330","display_name":"Hydrology and Watershed Management Studies","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2312","display_name":"Water Science and Technology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.9977999925613403,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10894","display_name":"Groundwater flow and contamination studies","score":0.991100013256073,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.7992249727249146},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6626732349395752},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5911344289779663},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5344889163970947},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.49539363384246826},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.47323474287986755},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4250166714191437},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1975897252559662}],"concepts":[{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.7992249727249146},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6626732349395752},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5911344289779663},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5344889163970947},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49539363384246826},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.47323474287986755},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4250166714191437},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1975897252559662},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2008.4633982","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2008.4633982","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1487923163","https://openalex.org/W1608030021","https://openalex.org/W1615539232","https://openalex.org/W1996210406","https://openalex.org/W1996747841","https://openalex.org/W2010275421","https://openalex.org/W2033904036","https://openalex.org/W2036056913","https://openalex.org/W2041420198","https://openalex.org/W2055336003","https://openalex.org/W2099485138","https://openalex.org/W2113076747","https://openalex.org/W2124738823"],"related_works":["https://openalex.org/W3162204513","https://openalex.org/W2371138613","https://openalex.org/W2048963458","https://openalex.org/W43109613","https://openalex.org/W2359952343","https://openalex.org/W2080152487","https://openalex.org/W2239445980","https://openalex.org/W3083152911","https://openalex.org/W2120455979","https://openalex.org/W3022347918"],"abstract_inverted_index":{"The":[0,68],"paper":[1],"presents":[2],"a":[3,73],"generalization":[4],"of":[5,10,34,44,51,83],"the":[6,42,84,88],"framework":[7],"for":[8],"assessment":[9],"predictive":[11],"models":[12],"uncertainty":[13,52],"using":[14],"machine":[15,63],"learning":[16,64],"techniques.":[17],"Historical":[18],"model":[19,36,54,56,76],"errors":[20],"which":[21],"are":[22,29,66],"mismatch":[23],"between":[24],"observed":[25],"and":[26,47,59],"predicted":[27],"values":[28],"assumed":[30],"to":[31,79],"be":[32],"indicators":[33],"total":[35],"uncertainty;":[37],"it":[38],"is":[39,70],"measured":[40],"in":[41,87],"form":[43],"prediction":[45],"intervals,":[46],"comprises":[48],"all":[49],"sources":[50],"including":[53],"structure,":[55],"parameters,":[57],"input":[58],"output":[60],"data.":[61],"Several":[62],"methods":[65],"compared.":[67],"approach":[69],"tested":[71],"on":[72],"conceptual":[74],"hydrological":[75],"set":[77],"up":[78],"predict":[80],"stream":[81],"flows":[82],"Brue":[85],"catchment":[86],"United":[89],"Kingdom.":[90]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
