{"id":"https://openalex.org/W4386352609","doi":"https://doi.org/10.1109/agro-geoinformatics59224.2023.10233283","title":"Research on the Optimization of Sample Point Placement for Ground Substrate Survey based on Interpretable Machine Learning","display_name":"Research on the Optimization of Sample Point Placement for Ground Substrate Survey based on Interpretable Machine Learning","publication_year":2023,"publication_date":"2023-07-25","ids":{"openalex":"https://openalex.org/W4386352609","doi":"https://doi.org/10.1109/agro-geoinformatics59224.2023.10233283"},"language":"en","primary_location":{"id":"doi:10.1109/agro-geoinformatics59224.2023.10233283","is_oa":false,"landing_page_url":"https://doi.org/10.1109/agro-geoinformatics59224.2023.10233283","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 11th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","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/A5057391234","display_name":"Yulei Tang","orcid":null},"institutions":[{"id":"https://openalex.org/I2799486974","display_name":"China Geological Survey","ror":"https://ror.org/04wtq2305","country_code":"CN","type":"other","lineage":["https://openalex.org/I2799486974"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"YuLei Tang","raw_affiliation_strings":["China Geological Survey,Center for Geophysical Survey,Langfang,China","Center for Geophysical Survey, China Geological Survey, Langfang, China"],"affiliations":[{"raw_affiliation_string":"China Geological Survey,Center for Geophysical Survey,Langfang,China","institution_ids":["https://openalex.org/I2799486974"]},{"raw_affiliation_string":"Center for Geophysical Survey, China Geological Survey, Langfang, China","institution_ids":["https://openalex.org/I2799486974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054499178","display_name":"Baowei Zhang","orcid":"https://orcid.org/0000-0003-4289-4754"},"institutions":[{"id":"https://openalex.org/I2799486974","display_name":"China Geological Survey","ror":"https://ror.org/04wtq2305","country_code":"CN","type":"other","lineage":["https://openalex.org/I2799486974"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Baowei Zhang","raw_affiliation_strings":["China Geological Survey,Center for Geophysical Survey,Langfang,China","Center for Geophysical Survey, China Geological Survey, Langfang, China"],"affiliations":[{"raw_affiliation_string":"China Geological Survey,Center for Geophysical Survey,Langfang,China","institution_ids":["https://openalex.org/I2799486974"]},{"raw_affiliation_string":"Center for Geophysical Survey, China Geological Survey, Langfang, China","institution_ids":["https://openalex.org/I2799486974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009436034","display_name":"Minhua Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I2799486974","display_name":"China Geological Survey","ror":"https://ror.org/04wtq2305","country_code":"CN","type":"other","lineage":["https://openalex.org/I2799486974"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Minhua Wang","raw_affiliation_strings":["China Geological Survey,Center for Geophysical Survey,Langfang,China","Center for Geophysical Survey, China Geological Survey, Langfang, China"],"affiliations":[{"raw_affiliation_string":"China Geological Survey,Center for Geophysical Survey,Langfang,China","institution_ids":["https://openalex.org/I2799486974"]},{"raw_affiliation_string":"Center for Geophysical Survey, China Geological Survey, Langfang, China","institution_ids":["https://openalex.org/I2799486974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104112793","display_name":"Qin Tian","orcid":null},"institutions":[{"id":"https://openalex.org/I2799486974","display_name":"China Geological Survey","ror":"https://ror.org/04wtq2305","country_code":"CN","type":"other","lineage":["https://openalex.org/I2799486974"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qin Tian","raw_affiliation_strings":["China Geological Survey,Langfang Center for General Survey of Natural Resources,Langfang,China","Langfang Center for General Survey of Natural Resources, China Geological Survey, Langfang, China"],"affiliations":[{"raw_affiliation_string":"China Geological Survey,Langfang Center for General Survey of Natural Resources,Langfang,China","institution_ids":["https://openalex.org/I2799486974"]},{"raw_affiliation_string":"Langfang Center for General Survey of Natural Resources, China Geological Survey, Langfang, China","institution_ids":["https://openalex.org/I2799486974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102942362","display_name":"Yichen Lu","orcid":"https://orcid.org/0000-0003-0296-3540"},"institutions":[{"id":"https://openalex.org/I45129253","display_name":"University College London","ror":"https://ror.org/02jx3x895","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I45129253"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Yichen Lu","raw_affiliation_strings":["University College London,London,UK","University College London, London, UK"],"affiliations":[{"raw_affiliation_string":"University College London,London,UK","institution_ids":["https://openalex.org/I45129253"]},{"raw_affiliation_string":"University College London, London, UK","institution_ids":["https://openalex.org/I45129253"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5057391234"],"corresponding_institution_ids":["https://openalex.org/I2799486974"],"apc_list":null,"apc_paid":null,"fwci":0.1257,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.42635467,"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":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9932000041007996,"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9932000041007996,"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/T10770","display_name":"Soil Geostatistics and Mapping","score":0.9502000212669373,"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.9286999702453613,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"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/sample","display_name":"Sample (material)","score":0.6584653258323669},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.6541038751602173},{"id":"https://openalex.org/keywords/substrate","display_name":"Substrate (aquarium)","score":0.6025166511535645},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5706208348274231},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46115589141845703},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40853965282440186},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.17610520124435425},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1740749180316925},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.1358233392238617},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.08579480648040771}],"concepts":[{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.6584653258323669},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.6541038751602173},{"id":"https://openalex.org/C2777289219","wikidata":"https://www.wikidata.org/wiki/Q7632154","display_name":"Substrate (aquarium)","level":2,"score":0.6025166511535645},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5706208348274231},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46115589141845703},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40853965282440186},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.17610520124435425},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1740749180316925},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.1358233392238617},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.08579480648040771},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/agro-geoinformatics59224.2023.10233283","is_oa":false,"landing_page_url":"https://doi.org/10.1109/agro-geoinformatics59224.2023.10233283","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 11th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Life in Land","score":0.4699999988079071,"id":"https://metadata.un.org/sdg/15"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320334926","display_name":"China Geological Survey","ror":"https://ror.org/04wtq2305"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1967926010","https://openalex.org/W2067220769","https://openalex.org/W2157395790","https://openalex.org/W2261059368","https://openalex.org/W2562199698","https://openalex.org/W2736370152","https://openalex.org/W2763486888","https://openalex.org/W2770188460","https://openalex.org/W2911964244","https://openalex.org/W2969945713","https://openalex.org/W2971890297","https://openalex.org/W3094683081","https://openalex.org/W3094704314","https://openalex.org/W3102027041","https://openalex.org/W3153263790","https://openalex.org/W3200460086","https://openalex.org/W4213128570","https://openalex.org/W4214866155","https://openalex.org/W4283318470","https://openalex.org/W4288987431","https://openalex.org/W4288987532"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W3107602296","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"To":[0],"obtain":[1],"comprehensive":[2],"and":[3,20,29,43,46,62,82,101,117,137,142,147,176,190,194,230,270,283,313,325,341,349,374,395,400,413,420,447],"accurate":[4],"data,":[5,112],"it":[6],"is":[7,26,88,249,441],"necessary":[8],"to":[9,91,104,126,152,186,238,369,417],"conduct":[10],"field":[11],"surveys":[12,446],"of":[13,58,68,111,268,333,336,355,358,430],"ground":[14,293,444],"substrate,":[15],"arable":[16],"land,":[17],"soil":[18,224,232,393],"quality,":[19],"other":[21,63],"natural":[22],"resource":[23],"classes.":[24],"This":[25],"in":[27,41,256,273,405],"difficult":[28],"key":[30],"areas.":[31],"Manual":[32],"sweeping":[33],"can":[34,51,75,97,119,244,414],"improve":[35],"survey":[36,49,254,295,319],"accuracy,":[37],"but":[38],"the":[39,47,56,66,173,207,241,274,280,310,314,326,342,356,362,384,406,428],"investment":[40],"human":[42],"material":[44],"resources,":[45],"long":[48],"cycle":[50],"complicate":[52],"taking":[53],"into":[54,171],"account":[55],"impact":[57],"geological,":[59],"hydrological,":[60],"soil,":[61,115],"factors":[64,386],"on":[65,78,145,217,436],"deployment":[67,389],"sample":[69,106,139,198,376,432],"points.":[70,107],"In":[71,150,202],"addition,":[72],"technical":[73],"experts":[74],"be":[76,120,184,245,415],"relied":[77,216],"for":[79,132,251,291,443],"expertise,":[80],"professionalism,":[81],"a":[83,162,180,266,337],"scientific":[84,102],"approach.":[85],"However,":[86],"generalizability":[87,412],"weak":[89],"due":[90],"its":[92],"subjective":[93],"nature.":[94],"Machine":[95],"learning":[96,124,167],"form":[98],"an":[99,330],"efficient":[100],"scheme":[103,307,317,345,363],"deploy":[105],"A":[108,247],"large":[109],"amount":[110],"such":[113,221],"as":[114,161,222],"geology,":[116],"meteorology,":[118],"simulated":[121],"using":[122,235,259],"machine":[123,166,438],"models":[125,156],"generate":[127],"effective":[128],"multidimensional":[129],"data":[130,214],"features":[131],"engineering,":[133],"identify":[134,187],"potential":[135,188],"patterns,":[136],"optimize":[138],"point":[140,199,296,343,351,377,388,433,448],"locations":[141],"quantities":[143],"based":[144,435],"nonlinear":[146],"higher-order":[148],"interactions.":[149],"contrast":[151],"traditional":[153],"black":[154],"box":[155],"that":[157,383],"use":[158],"prediction":[159],"accuracy":[160],"single":[163],"metric,":[164],"interpretable":[165,437],"(IML)":[168],"provides":[169],"insight":[170],"how":[172],"model":[174],"learns":[175],"makes":[177],"predictions.":[178],"As":[179,353,397],"result,":[181],"we":[182,205],"will":[183],"able":[185],"biases":[189],"errors":[191],"more":[192,196],"easily":[193],"build":[195],"reliable":[197],"placement":[200,297,344,434],"models.":[201],"this":[203],"study,":[204],"proposed":[206],"RF-GA":[208,311,327,380,409,431],"algorithm,":[209],"which":[210,277,440],"integrates":[211],"environmental":[212],"factor":[213,262],"often":[215],"by":[218,309,323],"experts\u2019":[219],"judgments,":[220,361],"topography,":[223,391],"type,":[225],"elevation,":[226],"slope,":[227],"surface":[228,252,339],"cover,":[229,340],"historical":[231,392],"texture.":[233],"By":[234],"genetic":[236],"algorithms":[237],"filter":[239],"variables,":[240],"feature":[242],"space":[243],"optimized.":[246],"plan":[248,290],"developed":[250],"matrix":[253],"points":[255,335,366],"northeast":[257],"Tieling":[258,275],"intermediate":[260],"scale":[261],"modeling.":[263],"There":[264],"are":[265,321,390,403],"variety":[267],"ecosystems":[269],"topography":[271],"types":[272],"area,":[276,408],"lies":[278],"between":[279,423],"Liaohe":[281],"Plain":[282],"Liaodong":[284],"mountainous":[285],"hilly":[286,401],"area.":[287],"An":[288],"optimized":[289,306,364],"regional":[292],"substrate":[294,445],"was":[298],"finally":[299],"determined":[300],"after":[301],"nearly":[302],"110,000":[303],"calculations.":[304],"The":[305,379],"generated":[308],"algorithm":[312,381],"expert":[315],"manual":[316],"(1:250,00":[318],"accuracy)":[320],"similar":[322,359],"87%,":[324],"rule":[328],"adopts":[329],"iterative":[331],"progression":[332],"prime":[334],"certain":[338],"reflects":[346],"improved":[347],"refinement":[348],"sampling":[350],"representativeness.":[352],"part":[354],"exploration":[357],"environment":[360],"785":[365],"(swept":[367],"survey)":[368],"136,":[370],"utilizing":[371],"zonal":[372],"classification":[373],"hierarchical":[375],"deployment.":[378],"found":[382],"main":[385],"affecting":[387],"texture,":[394],"slope.":[396],"both":[398],"plain":[399],"terrain":[402],"considered":[404],"study":[407],"has":[410],"high":[411],"applied":[416],"plains,":[418],"hills,":[419],"transition":[421],"zones":[422],"them.":[424],"We":[425],"have":[426],"demonstrated":[427],"feasibility":[429],"learning,":[439],"valuable":[442],"optimization":[449],"research.":[450]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}