{"id":"https://openalex.org/W3025415229","doi":"https://doi.org/10.3390/rs12101567","title":"Retrieval of Water Quality Parameters from Hyperspectral Images Using Hybrid Bayesian Probabilistic Neural Network","display_name":"Retrieval of Water Quality Parameters from Hyperspectral Images Using Hybrid Bayesian Probabilistic Neural Network","publication_year":2020,"publication_date":"2020-05-14","ids":{"openalex":"https://openalex.org/W3025415229","doi":"https://doi.org/10.3390/rs12101567","mag":"3025415229"},"language":"en","primary_location":{"id":"doi:10.3390/rs12101567","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs12101567","pdf_url":"https://www.mdpi.com/2072-4292/12/10/1567/pdf?version=1590669938","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2072-4292/12/10/1567/pdf?version=1590669938","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5074824871","display_name":"Yishan Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yishan Zhang","raw_affiliation_strings":["Institute of Remote Sensing and Geographical Information Science, School of Earth and Space Sciences, Peking University, Beijing 100871, China"],"affiliations":[{"raw_affiliation_string":"Institute of Remote Sensing and Geographical Information Science, School of Earth and Space Sciences, Peking University, Beijing 100871, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108526380","display_name":"Lun Wu","orcid":"https://orcid.org/0000-0001-5307-8969"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Lun Wu","raw_affiliation_strings":["Institute of Remote Sensing and Geographical Information Science, School of Earth and Space Sciences, Peking University, Beijing 100871, China"],"affiliations":[{"raw_affiliation_string":"Institute of Remote Sensing and Geographical Information Science, School of Earth and Space Sciences, Peking University, Beijing 100871, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081742589","display_name":"Huazhong Ren","orcid":"https://orcid.org/0000-0002-2882-308X"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huazhong Ren","raw_affiliation_strings":["Institute of Remote Sensing and Geographical Information Science, School of Earth and Space Sciences, Peking University, Beijing 100871, China"],"affiliations":[{"raw_affiliation_string":"Institute of Remote Sensing and Geographical Information Science, School of Earth and Space Sciences, Peking University, Beijing 100871, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024739229","display_name":"Licui Deng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Licui Deng","raw_affiliation_strings":["Shenzhen Huahan Technology Company, Shenzhen 518057, China"],"affiliations":[{"raw_affiliation_string":"Shenzhen Huahan Technology Company, Shenzhen 518057, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035407903","display_name":"Pengcheng Zhang","orcid":"https://orcid.org/0000-0003-3594-408X"},"institutions":[{"id":"https://openalex.org/I4210126705","display_name":"Guangzhou Urban Planning Survey & Design Institute","ror":"https://ror.org/02crg7060","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210126705"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pengcheng Zhang","raw_affiliation_strings":["Guangzhou Urban Planning & Design Survey Research Institute, Guangzhou 510060, China"],"affiliations":[{"raw_affiliation_string":"Guangzhou Urban Planning & Design Survey Research Institute, Guangzhou 510060, China","institution_ids":["https://openalex.org/I4210126705"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5108526380"],"corresponding_institution_ids":["https://openalex.org/I20231570"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":3.1549,"has_fulltext":true,"cited_by_count":44,"citation_normalized_percentile":{"value":0.91020408,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"12","issue":"10","first_page":"1567","last_page":"1567"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T14249","display_name":"Water Quality Monitoring and Analysis","score":0.9968000054359436,"subfield":{"id":"https://openalex.org/subfields/2311","display_name":"Industrial and Manufacturing 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/T14249","display_name":"Water Quality Monitoring and Analysis","score":0.9968000054359436,"subfield":{"id":"https://openalex.org/subfields/2311","display_name":"Industrial and Manufacturing 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/T12697","display_name":"Water Quality Monitoring Technologies","score":0.9952999949455261,"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/T11634","display_name":"Water Quality and Pollution Assessment","score":0.9884999990463257,"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/chemical-oxygen-demand","display_name":"Chemical oxygen demand","score":0.7561757564544678},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.6791030168533325},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.5790101289749146},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5516237616539001},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5473669767379761},{"id":"https://openalex.org/keywords/biochemical-oxygen-demand","display_name":"Biochemical oxygen demand","score":0.5461264252662659},{"id":"https://openalex.org/keywords/water-quality","display_name":"Water quality","score":0.5433614253997803},{"id":"https://openalex.org/keywords/endmember","display_name":"Endmember","score":0.5076280832290649},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4907110929489136},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.43578100204467773},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.4325333535671234},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.4214581251144409},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4117926359176636},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3506595492362976},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3363698124885559},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2786474823951721},{"id":"https://openalex.org/keywords/environmental-engineering","display_name":"Environmental engineering","score":0.09856820106506348},{"id":"https://openalex.org/keywords/ecology","display_name":"Ecology","score":0.09098580479621887}],"concepts":[{"id":"https://openalex.org/C188287460","wikidata":"https://www.wikidata.org/wiki/Q555857","display_name":"Chemical oxygen demand","level":3,"score":0.7561757564544678},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.6791030168533325},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.5790101289749146},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5516237616539001},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5473669767379761},{"id":"https://openalex.org/C198302294","wikidata":"https://www.wikidata.org/wiki/Q215624","display_name":"Biochemical oxygen demand","level":4,"score":0.5461264252662659},{"id":"https://openalex.org/C2780797713","wikidata":"https://www.wikidata.org/wiki/Q625376","display_name":"Water quality","level":2,"score":0.5433614253997803},{"id":"https://openalex.org/C58237817","wikidata":"https://www.wikidata.org/wiki/Q5376204","display_name":"Endmember","level":3,"score":0.5076280832290649},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4907110929489136},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.43578100204467773},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.4325333535671234},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.4214581251144409},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4117926359176636},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3506595492362976},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3363698124885559},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2786474823951721},{"id":"https://openalex.org/C87717796","wikidata":"https://www.wikidata.org/wiki/Q146326","display_name":"Environmental engineering","level":1,"score":0.09856820106506348},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.09098580479621887},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C94061648","wikidata":"https://www.wikidata.org/wiki/Q336191","display_name":"Wastewater","level":2,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/rs12101567","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs12101567","pdf_url":"https://www.mdpi.com/2072-4292/12/10/1567/pdf?version=1590669938","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:3dbc47599b674b00acb6bb99ca885f33","is_oa":true,"landing_page_url":"https://doaj.org/article/3dbc47599b674b00acb6bb99ca885f33","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing, Vol 12, Iss 10, p 1567 (2020)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/12/10/1567/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs12101567","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing; Volume 12; Issue 10; Pages: 1567","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/rs12101567","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs12101567","pdf_url":"https://www.mdpi.com/2072-4292/12/10/1567/pdf?version=1590669938","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/6","display_name":"Clean water and sanitation","score":0.8600000143051147}],"awards":[{"id":"https://openalex.org/G2087396116","display_name":null,"funder_award_id":"China","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2981938667","display_name":null,"funder_award_id":"Shenzhen","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G3317480652","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G391238517","display_name":null,"funder_award_id":", and","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4438666579","display_name":null,"funder_award_id":"No. 41771369","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5939423041","display_name":null,"funder_award_id":"Technology","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5994120800","display_name":null,"funder_award_id":"Natural","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8233805494","display_name":null,"funder_award_id":"41771369","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3025415229.pdf","grobid_xml":"https://content.openalex.org/works/W3025415229.grobid-xml"},"referenced_works_count":77,"referenced_works":["https://openalex.org/W1516111018","https://openalex.org/W1638753340","https://openalex.org/W1964810204","https://openalex.org/W1970030194","https://openalex.org/W1972392539","https://openalex.org/W1975731452","https://openalex.org/W1982837869","https://openalex.org/W1985151592","https://openalex.org/W1990872105","https://openalex.org/W1993803919","https://openalex.org/W2018299594","https://openalex.org/W2025300043","https://openalex.org/W2026440158","https://openalex.org/W2031084497","https://openalex.org/W2032422486","https://openalex.org/W2033003384","https://openalex.org/W2037576118","https://openalex.org/W2039591827","https://openalex.org/W2040738344","https://openalex.org/W2042040410","https://openalex.org/W2044771305","https://openalex.org/W2057320569","https://openalex.org/W2057683300","https://openalex.org/W2059383278","https://openalex.org/W2066485336","https://openalex.org/W2070665440","https://openalex.org/W2072927553","https://openalex.org/W2079760223","https://openalex.org/W2095473880","https://openalex.org/W2108584354","https://openalex.org/W2113051562","https://openalex.org/W2125240520","https://openalex.org/W2131659431","https://openalex.org/W2136903247","https://openalex.org/W2144731007","https://openalex.org/W2148671463","https://openalex.org/W2154647733","https://openalex.org/W2155420619","https://openalex.org/W2159574795","https://openalex.org/W2166946385","https://openalex.org/W2221419061","https://openalex.org/W2253137654","https://openalex.org/W2444185468","https://openalex.org/W2499899645","https://openalex.org/W2513884524","https://openalex.org/W2517986957","https://openalex.org/W2524622917","https://openalex.org/W2571174014","https://openalex.org/W2590806697","https://openalex.org/W2592370510","https://openalex.org/W2620965429","https://openalex.org/W2698520966","https://openalex.org/W2757850880","https://openalex.org/W2905168018","https://openalex.org/W2913506891","https://openalex.org/W2953928051","https://openalex.org/W2962994101","https://openalex.org/W2964110223","https://openalex.org/W2965689387","https://openalex.org/W2967229946","https://openalex.org/W2971752124","https://openalex.org/W2971760542","https://openalex.org/W2972020490","https://openalex.org/W2975015875","https://openalex.org/W2984318523","https://openalex.org/W2988720519","https://openalex.org/W2994688647","https://openalex.org/W3001145258","https://openalex.org/W3003812244","https://openalex.org/W3015122510","https://openalex.org/W3104008686","https://openalex.org/W4229686535","https://openalex.org/W4245997884","https://openalex.org/W4247571890","https://openalex.org/W6661457130","https://openalex.org/W6696727771","https://openalex.org/W6734070889"],"related_works":["https://openalex.org/W2037328426","https://openalex.org/W1990914742","https://openalex.org/W3106536224","https://openalex.org/W2891033441","https://openalex.org/W2890371384","https://openalex.org/W2315521504","https://openalex.org/W2006559622","https://openalex.org/W2040756827","https://openalex.org/W2051769241","https://openalex.org/W2773863718"],"abstract_inverted_index":{"The":[0,64,112,148,181],"protection":[1],"of":[2,6,90,107,119,126,151,187,222,243,273,283,320,329],"water":[3,17,47,109,285,310],"resources":[4],"is":[5,155,236,254,292],"paramount":[7],"importance":[8],"to":[9,44,164,178,227,268,278,295,307],"human":[10],"beings\u2019":[11],"practical":[12],"lives.":[13],"Monitoring":[14],"and":[15,61,76,101,104,168,192,204,232,256,265,316,333],"improving":[16],"quality":[18,48,110,286,311],"nowadays":[19],"has":[20,41],"become":[21],"an":[22,160,301],"important":[23,161],"topic.":[24],"In":[25,208],"this":[26,215],"study,":[27],"a":[28,218,247,262,313],"novel":[29],"Bayesian":[30,38,70,145],"probabilistic":[31],"neural":[32,78],"network":[33],"(BPNN)":[34],"improved":[35],"from":[36,88,137,175],"ordinary":[37,144],"probability":[39,71,146],"methods":[40],"been":[42],"developed":[43],"quantitatively":[45],"predict":[46],"parameters":[49],"including":[50],"phosphorus,":[51,188],"nitrogen,":[52,189],"chemical":[53],"oxygen":[54,58],"demand":[55,59],"(COD),":[56],"biochemical":[57],"(BOD),":[60],"chlorophyll":[62],"a.":[63],"proposed":[65,121,230,234,252,290],"method,":[66],"based":[67,98],"on":[68,99,239,299,312],"conventional":[69,138],"methods,":[72,139,214],"involves":[73],"feature":[74,93],"engineering":[75],"deep":[77,212],"networks.":[79],"Additionally,":[80],"it":[81],"extracts":[82],"significant":[83,334],"information":[84],"for":[85],"each":[86,106,284],"endmember":[87],"combinations":[89,272],"spectra":[91],"by":[92],"extraction,":[94],"with":[95,123,210,270],"spectral":[96],"unmixing":[97],"mathematical":[100],"statistical":[102,162],"analysis,":[103],"calculates":[105],"the":[108,116,120,134,229,233,240,251,280,289,309,318,324],"parameters.":[111],"experimental":[113],"results":[114,170],"show":[115,171],"great":[117],"performance":[118],"model":[122,166,231,235],"all":[124],"coefficient":[125],"determination":[127],"R":[128],"2":[129],"over":[130],"0.9":[131],"greater":[132,142,248],"than":[133,143],"values":[135],"(0.6\u20130.8)":[136],"which":[140],"are":[141,195],"analysis.":[147],"mean":[149,183],"percent":[150],"absolute":[152],"error":[153],"(MPAE)":[154],"taken":[156],"into":[157],"account":[158],"as":[159,224],"criterion":[163],"evaluate":[165],"performance,":[167],"our":[169],"that":[172],"MPAE":[173],"ranges":[174],"4%":[176],"(nitrogen)":[177],"10%":[179],"(COD).":[180],"root":[182],"squared":[184],"errors":[185],"(RMSEs)":[186],"COD,":[190],"BOD,":[191],"chlorophyll-a":[193],"(Chla)":[194],"0.03":[196],"mg/L,":[197,199,201,203],"0.28":[198],"3.28":[200],"0.49":[202],"0.75":[205],"\u03bcg/L,":[206],"respectively.":[207],"comparison":[209],"other":[211],"learning":[213],"study":[216,260],"takes":[217],"relatively":[219],"small":[220],"amount":[221,242],"data":[223,226,298],"training":[225],"train":[228],"then":[237],"tested":[238],"same":[241],"testing":[244],"data,":[245],"achieving":[246],"performance.":[249],"Thus,":[250],"method":[253,267,291],"time-saving":[255],"more":[257,263],"effective.":[258],"This":[259],"proposes":[261],"compatible":[264],"effective":[266],"assist":[269],"decomposing":[271],"hyperspectral":[274,296],"signatures":[275],"in":[276,305,323],"order":[277,306],"calculate":[279],"content":[281],"level":[282],"parameter.":[287],"Moreover,":[288],"practically":[293],"applied":[294],"image":[297],"board":[300],"unmanned":[302],"aerial":[303],"vehicle":[304],"monitor":[308],"large":[314],"scale":[315],"trace":[317],"location":[319],"pollution":[321],"sources":[322],"Maozhou":[325],"River,":[326],"Guangdong":[327],"Province":[328],"China,":[330],"obtaining":[331],"well-explained":[332],"results.":[335]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":11},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":11},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2}],"updated_date":"2026-04-12T07:58:50.170612","created_date":"2025-10-10T00:00:00"}