{"id":"https://openalex.org/W7204459151","doi":"https://doi.org/10.1080/03071375.2026.2722875","title":"Quantitative assessment of urban tree fall risk using ensemble learning: integrating structural, defect, and meteorological features","display_name":"Quantitative assessment of urban tree fall risk using ensemble learning: integrating structural, defect, and meteorological features","publication_year":2026,"publication_date":"2026-08-27","ids":{"openalex":"https://openalex.org/W7204459151","doi":"https://doi.org/10.1080/03071375.2026.2722875"},"language":"en","primary_location":{"id":"doi:10.1080/03071375.2026.2722875","doi":"https://doi.org/10.1080/03071375.2026.2722875","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03071375.2026.2722875","pdf_url":null,"source":{"id":"https://openalex.org/S2764655933","display_name":"Arboricultural Journal","issn_l":"0003-7931","issn":["0003-7931","0307-1375","2168-1074"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-1"],"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547","https://openalex.org/P4310320449"],"host_organization_lineage_names":["Taylor & Francis","Informa"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Arboricultural Journal","raw_type":"journal-article"},"volume":null,"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/A5146560886","display_name":"Han Li","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4405275796","display_name":"Resp AI Research Lab (CIIOE)","ror":"https://ror.org/04a3kq444","country_code":"CN","type":"nonprofit","lineage":["https://openalex.org/I4405275796"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Han Li","raw_affiliation_strings":["Resp AI Research Lab"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Resp AI Research Lab","institution_ids":["https://openalex.org/I4405275796"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5128259597","display_name":"Xufeng Zhang","orcid":"https://orcid.org/0009-0006-4124-357X","observed_orcids":["https://orcid.org/0009-0006-4124-357X"]},"institutions":[{"id":"https://openalex.org/I4405275796","display_name":"Resp AI Research Lab (CIIOE)","ror":"https://ror.org/04a3kq444","country_code":"CN","type":"nonprofit","lineage":["https://openalex.org/I4405275796"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xufeng Zhang","raw_affiliation_strings":["Resp AI Research Lab"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Resp AI Research Lab","institution_ids":["https://openalex.org/I4405275796"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5146560886"],"corresponding_institution_ids":["https://openalex.org/I4405275796"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.6714775,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"11"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13801","display_name":"Urban Arborization and Environmental Studies","score":0.6190999746322632,"subfield":{"id":"https://openalex.org/subfields/2308","display_name":"Management, Monitoring, Policy and Law"},"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/T13801","display_name":"Urban Arborization and Environmental Studies","score":0.6190999746322632,"subfield":{"id":"https://openalex.org/subfields/2308","display_name":"Management, Monitoring, Policy and Law"},"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/T12729","display_name":"Tree Root and Stability Studies","score":0.37299999594688416,"subfield":{"id":"https://openalex.org/subfields/2210","display_name":"Mechanical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.0012000000569969416,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"ensemble learning","score":0.5809999704360962},{"id":"https://openalex.org/keywords/random-forest","display_name":"random forest","score":0.7519999742507935},{"id":"https://openalex.org/keywords/xgboost","display_name":"XGBoost","score":0.8259999752044678},{"id":"https://openalex.org/keywords/lightgbm","display_name":"LightGBM","score":0.8679999709129333},{"id":"https://openalex.org/keywords/stacking","display_name":"stacking","score":0.5059999823570251},{"id":"https://openalex.org/keywords/meteorological-variables","display_name":"meteorological parameters","score":0.7770000100135803},{"id":"https://openalex.org/keywords/decision-support","display_name":"decision support","score":0.39899998903274536},{"id":"https://openalex.org/keywords/tree-species","display_name":"tree species","score":0.3970000147819519},{"id":"https://openalex.org/keywords/diameter-at-breast-height","display_name":"diameter at breast height","score":0.7919999957084656}],"concepts":[{"id":"https://openalex.org/C2984588014","wikidata":"https://www.wikidata.org/wiki/Q730675","display_name":"Quantitative assessment","level":2,"score":0.791100025177002},{"id":"https://openalex.org/C12174686","wikidata":"https://www.wikidata.org/wiki/Q1058438","display_name":"Risk assessment","level":2,"score":0.5569000244140625},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.5205000042915344},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.4959999918937683},{"id":"https://openalex.org/C95986675","wikidata":"https://www.wikidata.org/wiki/Q185168","display_name":"Quantitative analysis (chemistry)","level":2,"score":0.4749000072479248},{"id":"https://openalex.org/C111874474","wikidata":"https://www.wikidata.org/wiki/Q6005872","display_name":"Impact assessment","level":2,"score":0.32760000228881836},{"id":"https://openalex.org/C2778061687","wikidata":"https://www.wikidata.org/wiki/Q5460077","display_name":"Flood risk assessment","level":3,"score":0.31209999322891235},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.3100999891757965},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.30000001192092896},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.2782000005245209},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.275299996137619},{"id":"https://openalex.org/C3020001037","wikidata":"https://www.wikidata.org/wiki/Q836575","display_name":"Quality assessment","level":3,"score":0.27219998836517334},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.26570001244544983},{"id":"https://openalex.org/C2777428918","wikidata":"https://www.wikidata.org/wiki/Q5690807","display_name":"Health assessment","level":2,"score":0.2623000144958496},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.2587999999523163},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.2529999911785126}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/03071375.2026.2722875","doi":"https://doi.org/10.1080/03071375.2026.2722875","is_oa":false,"landing_page_url":"https://doi.org/10.1080/03071375.2026.2722875","pdf_url":null,"source":{"id":"https://openalex.org/S2764655933","display_name":"Arboricultural Journal","issn_l":"0003-7931","issn":["0003-7931","0307-1375","2168-1074"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-1"],"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547","https://openalex.org/P4310320449"],"host_organization_lineage_names":["Taylor & Francis","Informa"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Arboricultural Journal","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.8787999749183655,"id":"https://metadata.un.org/sdg/11"}],"sustainable_development_goals_aurora":[{"display_name":"Sustainable cities and communities","score":0.7621363997459412,"id":"https://metadata.un.org/sdg/11"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W28412257","https://openalex.org/W1521267137","https://openalex.org/W1678356000","https://openalex.org/W1970452773","https://openalex.org/W1996656563","https://openalex.org/W2002027610","https://openalex.org/W2011736802","https://openalex.org/W2084375685","https://openalex.org/W2108077217","https://openalex.org/W2111065693","https://openalex.org/W2122167509","https://openalex.org/W2127476821","https://openalex.org/W2261218331","https://openalex.org/W2295598076","https://openalex.org/W2462610241","https://openalex.org/W2911964244","https://openalex.org/W2980634351","https://openalex.org/W4367184132","https://openalex.org/W4391018548","https://openalex.org/W4400191969","https://openalex.org/W4412700752","https://openalex.org/W7083015668","https://openalex.org/W7107871347"],"related_works":[],"abstract_inverted_index":{"Urban":[0],"trees":[1,105],"provide":[2],"critical":[3],"ecosystem":[4],"services":[5],"yet":[6],"pose":[7],"significant":[8],"public":[9],"safety":[10],"risks":[11],"when":[12,179,189],"structurally":[13],"compromised.":[14],"Existing":[15],"risk":[16,55,110],"assessment":[17],"frameworks":[18],"rely":[19],"primarily":[20],"on":[21],"on-site":[22],"arborist":[23,214],"inspection,":[24],"which":[25],"is":[26,198],"resource-intensive":[27],"and":[28,64,99,108,126,137,161,186],"difficult":[29],"to":[30,152],"scale":[31],"across":[32],"large":[33],"municipal":[34],"inventories.":[35],"This":[36],"study":[37],"develops":[38],"a":[39,135,176,202,210],"quantitative":[40],"fall-risk":[41],"classification":[42],"framework":[43,197],"using":[44],"5,000":[45],"New":[46],"York":[47],"City":[48],"Forestry":[49],"Tree":[50],"Points":[51],"with":[52],"recorded":[53],"professional":[54],"ratings,":[56],"structural":[57],"attributes,":[58],"species,":[59],"borough":[60,72,190],"assignment,":[61],"spatial":[62],"blocks,":[63],"date-aligned":[65],"NASA":[66],"POWER":[67],"meteorological":[68,131],"variables.":[69],"A":[70],"proportional":[71],"\u00d7":[73,75],"principal-species/Other":[74],"DBH-class":[76],"sampling":[77],"design":[78],"preserved":[79],"the":[80,117,155],"eligible":[81],"population":[82],"structure":[83],"while":[84],"supporting":[85],"transparent":[86],"model":[87,115],"evaluation.":[88],"Five-fold":[89],"spatial-block":[90],"validation":[91],"compared":[92],"Logistic":[93],"Regression,":[94],"Random":[95,113,142],"Forest,":[96],"XGBoost,":[97],"LightGBM,":[98],"Stacking":[100],"models":[101],"for":[102,157,205,212],"distinguishing":[103],"high-risk":[104],"from":[106],"low":[107],"moderate":[109],"trees.":[111],"The":[112,196],"Forest":[114,143],"provided":[116],"strongest":[118],"structural-only":[119],"ROC-AUC":[120,146],"(0.809;":[121],"95%":[122,149],"bootstrap":[123],"CI:":[124,150],"0.773\u20130.842)":[125],"PR-AUC":[127],"of":[128],"0.122.":[129],"Coarse":[130],"variables":[132],"yielded":[133],"only":[134],"small":[136],"uncertain":[138],"incremental":[139],"change":[140,169,188],"in":[141],"discrimination":[144],"(Delta":[145],"=":[147,174,184,194],"0.006;":[148],"\u22120.011":[151],"0.025),":[153],"emphasising":[154],"need":[156],"finer":[158],"storm,":[159],"wind-canyon,":[160],"root-zone":[162],"measurements.":[163],"Sensitivity":[164],"analyses":[165],"showed":[166],"modest":[167],"performance":[168],"after":[170],"excluding":[171],"Manhattan":[172],"(ROC-AUC":[173,183,193],"0.801),":[175],"larger":[177],"decrease":[178],"species":[180],"was":[181,191],"excluded":[182,192],"0.783),":[185],"minimal":[187],"0.804).":[195],"best":[199],"interpreted":[200],"as":[201],"decision-support":[203],"tool":[204],"inspection":[206],"prioritisation":[207],"rather":[208],"than":[209],"replacement":[211],"field":[213],"assessment.":[215]},"counts_by_year":[],"updated_date":"2026-10-01T08:54:57.152042","created_date":"2026-08-28T00:00:00"}
