{"id":"https://openalex.org/W3120032734","doi":"https://doi.org/10.1145/3429309.3429322","title":"Modeling watershed nutrient concentrations with AutoML","display_name":"Modeling watershed nutrient concentrations with AutoML","publication_year":2020,"publication_date":"2020-09-22","ids":{"openalex":"https://openalex.org/W3120032734","doi":"https://doi.org/10.1145/3429309.3429322","mag":"3120032734"},"language":"en","primary_location":{"id":"doi:10.1145/3429309.3429322","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3429309.3429322","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 10th International Conference on Climate Informatics","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/A5100403880","display_name":"Grace Kim","orcid":"https://orcid.org/0000-0003-0128-4853"},"institutions":[{"id":"https://openalex.org/I1322124587","display_name":"Booz Allen Hamilton (United States)","ror":"https://ror.org/051rcp357","country_code":"US","type":"company","lineage":["https://openalex.org/I1322124587"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Grace E. Kim","raw_affiliation_strings":["Booz Allen Hamilton, US"],"affiliations":[{"raw_affiliation_string":"Booz Allen Hamilton, US","institution_ids":["https://openalex.org/I1322124587"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014311303","display_name":"Moritz Steller","orcid":null},"institutions":[{"id":"https://openalex.org/I1322124587","display_name":"Booz Allen Hamilton (United States)","ror":"https://ror.org/051rcp357","country_code":"US","type":"company","lineage":["https://openalex.org/I1322124587"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Moritz Steller","raw_affiliation_strings":["Booz Allen Hamilton, US"],"affiliations":[{"raw_affiliation_string":"Booz Allen Hamilton, US","institution_ids":["https://openalex.org/I1322124587"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112599237","display_name":"Sarah Olson","orcid":null},"institutions":[{"id":"https://openalex.org/I1322124587","display_name":"Booz Allen Hamilton (United States)","ror":"https://ror.org/051rcp357","country_code":"US","type":"company","lineage":["https://openalex.org/I1322124587"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sarah Olson","raw_affiliation_strings":["Booz Allen Hamilton, US"],"affiliations":[{"raw_affiliation_string":"Booz Allen Hamilton, US","institution_ids":["https://openalex.org/I1322124587"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100403880"],"corresponding_institution_ids":["https://openalex.org/I1322124587"],"apc_list":null,"apc_paid":null,"fwci":0.0952,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.45410033,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"86","last_page":"90"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.9966999888420105,"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"}},"topics":[{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.9966999888420105,"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/T10330","display_name":"Hydrology and Watershed Management Studies","score":0.9955000281333923,"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/T10032","display_name":"Marine and coastal ecosystems","score":0.9900000095367432,"subfield":{"id":"https://openalex.org/subfields/1910","display_name":"Oceanography"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/watershed","display_name":"Watershed","score":0.8365349769592285},{"id":"https://openalex.org/keywords/water-quality","display_name":"Water quality","score":0.6827263832092285},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.5866838097572327},{"id":"https://openalex.org/keywords/hydrology","display_name":"Hydrology (agriculture)","score":0.46679574251174927},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.36296480894088745},{"id":"https://openalex.org/keywords/ecology","display_name":"Ecology","score":0.28856873512268066},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.19558271765708923},{"id":"https://openalex.org/keywords/biology","display_name":"Biology","score":0.11624425649642944}],"concepts":[{"id":"https://openalex.org/C150547873","wikidata":"https://www.wikidata.org/wiki/Q947851","display_name":"Watershed","level":2,"score":0.8365349769592285},{"id":"https://openalex.org/C2780797713","wikidata":"https://www.wikidata.org/wiki/Q625376","display_name":"Water quality","level":2,"score":0.6827263832092285},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.5866838097572327},{"id":"https://openalex.org/C76886044","wikidata":"https://www.wikidata.org/wiki/Q2883300","display_name":"Hydrology (agriculture)","level":2,"score":0.46679574251174927},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.36296480894088745},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.28856873512268066},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.19558271765708923},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.11624425649642944},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C187320778","wikidata":"https://www.wikidata.org/wiki/Q1349130","display_name":"Geotechnical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3429309.3429322","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3429309.3429322","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 10th International Conference on Climate Informatics","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6800000071525574,"display_name":"Clean water and sanitation","id":"https://metadata.un.org/sdg/6"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W2104658141","https://openalex.org/W2105103805","https://openalex.org/W2510491405","https://openalex.org/W2765885072","https://openalex.org/W6911078418"],"related_works":["https://openalex.org/W46140484","https://openalex.org/W1974344527","https://openalex.org/W2382634454","https://openalex.org/W2360298601","https://openalex.org/W2374021052","https://openalex.org/W2129907763","https://openalex.org/W2360606605","https://openalex.org/W2371680562","https://openalex.org/W4281621378","https://openalex.org/W2765512051"],"abstract_inverted_index":{"Water":[0],"quality":[1],"impairments":[2],"in":[3,25,101,147,152,183],"coastal":[4],"and":[5,37,75,93,109,130,201,218],"estuarine":[6],"waters":[7],"around":[8],"the":[9,65,73,90,102,135,148,153,168,174,184,214,245],"world":[10],"have":[11,57],"been":[12,58],"linked":[13],"to":[14,60,88,167,213],"human":[15],"activities":[16],"on":[17,134],"land.":[18],"Excess":[19],"nitrogen":[20,99,227],"inputs":[21],"from":[22,107,162],"rivers":[23],"result":[24],"low":[26],"dissolved":[27],"oxygen":[28],"bottom":[29,51],"waters,":[30],"or":[31],"hypoxia,":[32],"which":[33,144],"has":[34],"negative":[35],"ecological":[36],"economic":[38],"impacts.":[39],"Additionally,":[40],"warming":[41],"surface":[42],"water":[43,62],"temperatures":[44],"increase":[45],"stratification,":[46],"further":[47],"preventing":[48],"oxygenation":[49],"of":[50,77,128,132,216,240],"waters.":[52],"Land":[53],"use":[54,220],"management":[55],"policies":[56],"implemented":[59],"improve":[61],"quality,":[63],"but":[64],"solution":[66],"must":[67],"be":[68,160,255],"regionally":[69],"appropriate,":[70],"accounting":[71],"for":[72,96,173,224,237,257],"uniqueness":[74],"complexity":[76],"each":[78],"watershed.":[79],"In":[80],"this":[81],"study,":[82,241],"we":[83,165,242],"implement":[84],"an":[85,126],"AutoML":[86,122,154,186],"pipeline":[87],"find":[89],"best":[91,169,246],"model":[92,115,139,172,188,249],"associated":[94],"parameters":[95],"predicting":[97],"total":[98],"concentrations":[100],"Chesapeake":[103],"Bay,":[104],"USA":[105],"watershed":[106,202,226,235],"geographic":[108,196],"climate":[110,205],"predictors.":[111,177,209],"The":[112,178],"highest":[113],"performing":[114,170,247],"was":[116],"a":[117,222],"stacked":[118],"ensemble":[119,163],"model,":[120,248],"H2O":[121,185],"Regression":[123],"GLM,":[124],"with":[125],"R2":[127],"0.91":[129],"RMSE":[131,192],"0.48":[133],"test":[136],"data.":[137],"This":[138],"outperformed":[140],"deep":[141],"learning":[142],"models,":[143,164],"were":[145,195,207],"included":[146],"60+":[149],"models":[150],"evaluated":[151],"pipeline.":[155],"Since":[156],"feature":[157,252],"importance":[158,215,253],"cannot":[159],"deciphered":[161],"looked":[166],"non-ensemble":[171],"most":[175,180],"important":[176,181],"three":[179],"features":[182],"XGBoost":[187],"(R2":[189],"=":[190,193],"0.84,":[191],"0.69)":[194],"indicators":[197],"(i.e.":[198],"longitude,":[199],"latitude":[200],"code)":[203],"while":[204],"variables":[206],"poor":[208],"Our":[210],"results":[211],"point":[212],"location":[217],"land":[219],"within":[221],"subwatershed":[223],"modeling":[225],"concentrations.":[228],"While":[229],"these":[230],"findings":[231],"provide":[232],"insight":[233],"into":[234],"dynamics":[236],"our":[238],"region":[239],"hypothesize":[243],"that":[244],"performance":[250],"and/or":[251],"may":[254],"different":[256],"other":[258],"geographies.":[259]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"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"}