{"id":"https://openalex.org/W4387803491","doi":"https://doi.org/10.1109/igarss52108.2023.10281923","title":"Improving Extreme Value Prediction For Water Clarity Using Weighted Regression Models","display_name":"Improving Extreme Value Prediction For Water Clarity Using Weighted Regression Models","publication_year":2023,"publication_date":"2023-07-16","ids":{"openalex":"https://openalex.org/W4387803491","doi":"https://doi.org/10.1109/igarss52108.2023.10281923"},"language":"en","primary_location":{"id":"doi:10.1109/igarss52108.2023.10281923","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss52108.2023.10281923","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium","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/A5011229716","display_name":"William Daniels","orcid":"https://orcid.org/0000-0001-8752-5536"},"institutions":[{"id":"https://openalex.org/I111979921","display_name":"Northwestern University","ror":"https://ror.org/000e0be47","country_code":"US","type":"education","lineage":["https://openalex.org/I111979921"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"William Daniels","raw_affiliation_strings":["Northwestern University,Evanston,IL,60208"],"affiliations":[{"raw_affiliation_string":"Northwestern University,Evanston,IL,60208","institution_ids":["https://openalex.org/I111979921"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073466498","display_name":"Troy Ames","orcid":"https://orcid.org/0000-0001-8308-2467"},"institutions":[{"id":"https://openalex.org/I1306266525","display_name":"Goddard Space Flight Center","ror":"https://ror.org/0171mag52","country_code":"US","type":"facility","lineage":["https://openalex.org/I1306266525","https://openalex.org/I4210124779"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Troy Ames","raw_affiliation_strings":["NASA Goddard Space Flight Center,Greenbelt,MD,20771"],"affiliations":[{"raw_affiliation_string":"NASA Goddard Space Flight Center,Greenbelt,MD,20771","institution_ids":["https://openalex.org/I1306266525"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079239635","display_name":"J. Blake Clark","orcid":"https://orcid.org/0000-0002-8454-1458"},"institutions":[{"id":"https://openalex.org/I1306266525","display_name":"Goddard Space Flight Center","ror":"https://ror.org/0171mag52","country_code":"US","type":"facility","lineage":["https://openalex.org/I1306266525","https://openalex.org/I4210124779"]},{"id":"https://openalex.org/I79272384","display_name":"University of Maryland, Baltimore County","ror":"https://ror.org/02qskvh78","country_code":"US","type":"education","lineage":["https://openalex.org/I79272384"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"J. Blake Clark","raw_affiliation_strings":["NASA Goddard Space Flight Center,Greenbelt,MD,20771","University of Maryland Baltimore County, Baltimore, MD"],"affiliations":[{"raw_affiliation_string":"NASA Goddard Space Flight Center,Greenbelt,MD,20771","institution_ids":["https://openalex.org/I1306266525"]},{"raw_affiliation_string":"University of Maryland Baltimore County, Baltimore, MD","institution_ids":["https://openalex.org/I79272384"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033346103","display_name":"Stephanie Schollaert Uz","orcid":"https://orcid.org/0000-0002-0937-1487"},"institutions":[{"id":"https://openalex.org/I1306266525","display_name":"Goddard Space Flight Center","ror":"https://ror.org/0171mag52","country_code":"US","type":"facility","lineage":["https://openalex.org/I1306266525","https://openalex.org/I4210124779"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Stephanie Schollaert Uz","raw_affiliation_strings":["NASA Goddard Space Flight Center,Greenbelt,MD,20771"],"affiliations":[{"raw_affiliation_string":"NASA Goddard Space Flight Center,Greenbelt,MD,20771","institution_ids":["https://openalex.org/I1306266525"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5011229716"],"corresponding_institution_ids":["https://openalex.org/I111979921"],"apc_list":null,"apc_paid":null,"fwci":0.1256,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.43975722,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"4907","last_page":"4910"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.9957000017166138,"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":0.9957000017166138,"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/T10302","display_name":"Fish Ecology and Management Studies","score":0.9778000116348267,"subfield":{"id":"https://openalex.org/subfields/2309","display_name":"Nature and Landscape Conservation"},"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/T12697","display_name":"Water Quality Monitoring Technologies","score":0.9768000245094299,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6551474928855896},{"id":"https://openalex.org/keywords/clarity","display_name":"CLARITY","score":0.5757322311401367},{"id":"https://openalex.org/keywords/estimator","display_name":"Estimator","score":0.544409453868866},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.541792631149292},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5292108654975891},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.47184279561042786},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4369380474090576},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.41163554787635803},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.40246137976646423},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3851781487464905},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16585376858711243}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6551474928855896},{"id":"https://openalex.org/C2777146004","wikidata":"https://www.wikidata.org/wiki/Q14949826","display_name":"CLARITY","level":2,"score":0.5757322311401367},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.544409453868866},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.541792631149292},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5292108654975891},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.47184279561042786},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4369380474090576},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41163554787635803},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40246137976646423},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3851781487464905},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16585376858711243},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss52108.2023.10281923","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss52108.2023.10281923","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8399999737739563,"id":"https://metadata.un.org/sdg/6","display_name":"Clean water and sanitation"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W70001371","https://openalex.org/W1579853615","https://openalex.org/W2005823186","https://openalex.org/W3083510345","https://openalex.org/W3121117209","https://openalex.org/W3130448211","https://openalex.org/W3132250316","https://openalex.org/W3208625163","https://openalex.org/W6634817459","https://openalex.org/W6790920653"],"related_works":["https://openalex.org/W31220157","https://openalex.org/W2312753042","https://openalex.org/W4289356671","https://openalex.org/W2389155397","https://openalex.org/W2165884543","https://openalex.org/W3186837933","https://openalex.org/W2368989808","https://openalex.org/W2034959125","https://openalex.org/W2355687852","https://openalex.org/W2621086889"],"abstract_inverted_index":{"Previous":[0],"work":[1],"on":[2,91],"predicting":[3],"water":[4,95],"quality":[5],"indicators":[6],"has":[7],"mainly":[8],"consisted":[9],"of":[10,44,68,94],"using":[11,80],"both":[12],"semi-analytical":[13],"algorithms":[14],"(SAAs)":[15],"and":[16,38,105,119],"empirical":[17],"approaches,":[18],"but":[19],"recently":[20,82],"new":[21],"data-driven":[22,45,107],"machine":[23],"learning":[24],"approaches":[25,109],"such":[26],"as":[27],"neural-network-based":[28],"regression":[29,85,108],"models":[30,46],"are":[31,75,117,121],"increasingly":[32],"being":[33],"explored":[34],"for":[35,110],"their":[36,62],"utility":[37],"potential":[39],"adoption.":[40],"Although":[41],"these":[42,92],"types":[43,93],"may":[47],"achieve":[48],"higher":[49],"accuracy":[50],"compared":[51],"to":[52,60,87],"previous":[53],"methods,":[54],"they":[55],"can":[56],"also":[57],"be":[58],"prone":[59],"biasing":[61],"outputs":[63],"towards":[64],"the":[65,69,99],"mean":[66],"value":[67],"target":[70],"distribution":[71],"if":[72],"model":[73],"inputs":[74],"noisy.":[76],"This":[77],"paper":[78],"investigates":[79],"a":[81],"published":[83],"weighted":[84,106],"approach":[86],"alleviate":[88],"\"mean-centric\"":[89],"bias":[90],"clarity":[96],"estimators":[97],"in":[98],"Chesapeake":[100,111],"Bay.":[101],"Experiments":[102],"comparing":[103],"standard":[104],"Bay":[112],"Secchi":[113],"disk":[114],"depth":[115],"prediction":[116],"performed":[118],"results":[120],"discussed.":[122]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
