{"id":"https://openalex.org/W7164683750","doi":"https://doi.org/10.1111/tgis.70318","title":"Enhancing Artificial Neural Network Performance for Wildfire Susceptibility Mapping Using Bernstein\u2010Levy and Multi\u2010Population Differential Evolution Algorithms","display_name":"Enhancing Artificial Neural Network Performance for Wildfire Susceptibility Mapping Using Bernstein\u2010Levy and Multi\u2010Population Differential Evolution Algorithms","publication_year":2026,"publication_date":"2026-06-01","ids":{"openalex":"https://openalex.org/W7164683750","doi":"https://doi.org/10.1111/tgis.70318"},"language":"en","primary_location":{"id":"doi:10.1111/tgis.70318","is_oa":true,"landing_page_url":"https://doi.org/10.1111/tgis.70318","pdf_url":null,"source":{"id":"https://openalex.org/S859791518","display_name":"Transactions in GIS","issn_l":"1361-1682","issn":["1361-1682","1467-9671"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Transactions in GIS","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1111/tgis.70318","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5024345552","display_name":"Talha Ta\u015fkanat","orcid":"https://orcid.org/0000-0001-6273-9298"},"institutions":[{"id":"https://openalex.org/I87673952","display_name":"Erciyes University","ror":"https://ror.org/047g8vk19","country_code":"TR","type":"education","lineage":["https://openalex.org/I87673952"]}],"countries":["TR"],"is_corresponding":true,"raw_author_name":"Talha Ta\u015fkanat","raw_affiliation_strings":["Department of Geomatics Engineering, Engineering Faculty Erciyes University  Kayseri T\u00fcrkiye"],"raw_orcid":"https://orcid.org/0000-0001-6273-9298","affiliations":[{"raw_affiliation_string":"Department of Geomatics Engineering, Engineering Faculty Erciyes University  Kayseri T\u00fcrkiye","institution_ids":["https://openalex.org/I87673952"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5024345552"],"corresponding_institution_ids":["https://openalex.org/I87673952"],"apc_list":{"value":3450,"currency":"USD","value_usd":3450},"apc_paid":{"value":3450,"currency":"USD","value_usd":3450},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.76921731,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"30","issue":"4","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10555","display_name":"Fire effects on ecosystems","score":0.9934999942779541,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"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/T10555","display_name":"Fire effects on ecosystems","score":0.9934999942779541,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"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/T10535","display_name":"Landslides and related hazards","score":0.0024999999441206455,"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/T12597","display_name":"Fire Detection and Safety Systems","score":0.0007999999797903001,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/differential-evolution","display_name":"Differential evolution","score":0.7791000008583069},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6096000075340271},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.589900016784668},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5767999887466431},{"id":"https://openalex.org/keywords/evolutionary-algorithm","display_name":"Evolutionary algorithm","score":0.3935000002384186},{"id":"https://openalex.org/keywords/pairwise-comparison","display_name":"Pairwise comparison","score":0.38199999928474426},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.36570000648498535}],"concepts":[{"id":"https://openalex.org/C74750220","wikidata":"https://www.wikidata.org/wiki/Q2662197","display_name":"Differential evolution","level":2,"score":0.7791000008583069},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6096000075340271},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.589900016784668},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5767999887466431},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5023999810218811},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4968999922275543},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41929998993873596},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4189000129699707},{"id":"https://openalex.org/C159149176","wikidata":"https://www.wikidata.org/wiki/Q14489129","display_name":"Evolutionary algorithm","level":2,"score":0.3935000002384186},{"id":"https://openalex.org/C184898388","wikidata":"https://www.wikidata.org/wiki/Q1435712","display_name":"Pairwise comparison","level":2,"score":0.38199999928474426},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3797000050544739},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36570000648498535},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.34139999747276306},{"id":"https://openalex.org/C186282968","wikidata":"https://www.wikidata.org/wiki/Q1434261","display_name":"McNemar's test","level":2,"score":0.335099995136261},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.3255999982357025},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.3215000033378601},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3212999999523163},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.29330000281333923},{"id":"https://openalex.org/C206041023","wikidata":"https://www.wikidata.org/wiki/Q1751970","display_name":"Wilcoxon signed-rank test","level":3,"score":0.2856999933719635},{"id":"https://openalex.org/C109718341","wikidata":"https://www.wikidata.org/wiki/Q1385229","display_name":"Metaheuristic","level":2,"score":0.28459998965263367},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.2529999911785126},{"id":"https://openalex.org/C189285262","wikidata":"https://www.wikidata.org/wiki/Q1332350","display_name":"Multicollinearity","level":3,"score":0.2517000138759613}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1111/tgis.70318","is_oa":true,"landing_page_url":"https://doi.org/10.1111/tgis.70318","pdf_url":null,"source":{"id":"https://openalex.org/S859791518","display_name":"Transactions in GIS","issn_l":"1361-1682","issn":["1361-1682","1467-9671"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Transactions in GIS","raw_type":"journal-article"},{"id":"pmh:55dde54d-cfca-4452-b29d-22e9293133fc","is_oa":false,"landing_page_url":"https://avesis.erciyes.edu.tr/publication/details/55dde54d-cfca-4452-b29d-22e9293133fc/oai","pdf_url":null,"source":{"id":"https://openalex.org/S7407055139","display_name":"Erciyes University - AVESIS","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"doi:10.1111/tgis.70318","is_oa":true,"landing_page_url":"https://doi.org/10.1111/tgis.70318","pdf_url":null,"source":{"id":"https://openalex.org/S859791518","display_name":"Transactions in GIS","issn_l":"1361-1682","issn":["1361-1682","1467-9671"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320595","host_organization_name":"Wiley","host_organization_lineage":["https://openalex.org/P4310320595"],"host_organization_lineage_names":["Wiley"],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Transactions in GIS","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":54,"referenced_works":["https://openalex.org/W790676990","https://openalex.org/W1584308190","https://openalex.org/W1595159159","https://openalex.org/W1978717383","https://openalex.org/W1981646498","https://openalex.org/W1991269377","https://openalex.org/W1997741078","https://openalex.org/W2004709915","https://openalex.org/W2010150056","https://openalex.org/W2014513140","https://openalex.org/W2030318996","https://openalex.org/W2037081161","https://openalex.org/W2040594664","https://openalex.org/W2043964510","https://openalex.org/W2076812056","https://openalex.org/W2078619499","https://openalex.org/W2107930974","https://openalex.org/W2126059511","https://openalex.org/W2131107724","https://openalex.org/W2156909104","https://openalex.org/W2157835465","https://openalex.org/W2158698691","https://openalex.org/W2225976211","https://openalex.org/W2275605338","https://openalex.org/W2295598076","https://openalex.org/W2337403412","https://openalex.org/W2553858786","https://openalex.org/W2557002900","https://openalex.org/W2601486684","https://openalex.org/W2614464134","https://openalex.org/W2778978672","https://openalex.org/W2789751949","https://openalex.org/W2907066318","https://openalex.org/W2911964244","https://openalex.org/W2920455132","https://openalex.org/W2955057476","https://openalex.org/W2963008249","https://openalex.org/W2972175198","https://openalex.org/W3001555788","https://openalex.org/W3015900272","https://openalex.org/W3090951340","https://openalex.org/W3102380346","https://openalex.org/W3110345594","https://openalex.org/W3166182933","https://openalex.org/W3170215261","https://openalex.org/W3203068818","https://openalex.org/W4200612379","https://openalex.org/W4220656189","https://openalex.org/W4223923442","https://openalex.org/W4252281785","https://openalex.org/W4322102192","https://openalex.org/W4368408248","https://openalex.org/W4388288877","https://openalex.org/W4394858377"],"related_works":[],"abstract_inverted_index":{"ABSTRACT":[0],"Wildfire":[1],"susceptibility":[2,31],"mapping":[3],"(WSM)":[4],"is":[5,264],"critical":[6],"for":[7,231,266,271,277],"forest":[8],"management,":[9],"land\u2010use":[10],"planning,":[11],"and":[12,63,82,93,110,143,158,171,192,222,242,274],"disaster":[13],"risk":[14,268],"mitigation.":[15],"Although":[16],"hybrid":[17],"artificial":[18],"neural":[19],"network":[20],"(ANN)":[21],"models":[22],"optimized":[23],"by":[24,130],"metaheuristic":[25],"algorithms":[26],"are":[27,34,238],"increasingly":[28],"used":[29],"in":[30,97,118,261],"mapping,":[32],"they":[33],"often":[35],"evaluated":[36,136],"without":[37],"strong":[38],"machine":[39],"learning":[40],"benchmarks,":[41],"spatially":[42],"robust":[43],"validation,":[44],"or":[45],"statistical":[46],"significance":[47],"testing.":[48],"This":[49],"study":[50],"benchmarks":[51],"two":[52],"practically":[53],"parameter\u2010free":[54],"evolutionary":[55],"ANN":[56,61,67],"models,":[57],"Bernstein\u2013Levy":[58],"Differential":[59,65],"Evolution":[60,66],"(BDE\u2010ANN)":[62],"Multi\u2010Population":[64],"(MDE\u2010ANN),":[68],"against":[69],"Logistic":[70],"Regression,":[71],"Support":[72],"Vector":[73],"Machine":[74],"with":[75,248],"RBF":[76],"kernel,":[77],"Random":[78,190],"Forest,":[79],"XGBoost,":[80],"ANN,":[81],"standard":[83],"DE\u2010ANN.":[84],"A":[85],"balanced":[86,267],"spatial":[87,131,138],"dataset":[88],"consisting":[89],"of":[90,122],"272":[91,94],"wildfire":[92],"non\u2010wildfire":[95],"locations":[96],"\u00c7anakkale,":[98],"T\u00fcrkiye,":[99],"was":[100,115,135,194],"constructed":[101],"using":[102,137],"14":[103],"geo\u2010environmental":[104],"conditioning":[105],"factors.":[106],"After":[107],"multicollinearity":[108],"assessment":[109],"ablation":[111],"analysis,":[112],"average":[113],"temperature":[114],"excluded,":[116],"resulting":[117],"a":[119,205,257],"final":[120],"set":[121],"13":[123],"predictors.":[124],"To":[125],"reduce":[126],"optimistic":[127],"bias":[128],"caused":[129],"autocorrelation,":[132],"model":[133,145],"performance":[134,247],"block":[139],"cross\u2010validation.":[140],"Predictive":[141],"uncertainty":[142],"pairwise":[144],"differences":[146],"were":[147],"further":[148],"assessed":[149],"through":[150],"bootstrap":[151],"AUC":[152,176],"confidence":[153],"intervals,":[154],"Wilcoxon":[155],"signed\u2010rank":[156],"tests,":[157],"McNemar":[159],"tests.":[160],"The":[161],"results":[162],"showed":[163],"that":[164,199],"MDE\u2010ANN":[165,200,263],"achieved":[166],"the":[167,216,254],"highest":[168,217],"overall":[169],"discrimination":[170],"lowest":[172],"prediction":[173],"error":[174],"(Test":[175],"=":[177,182],"0.874":[178],"\u00b1":[179,184,220,225],"0.064;":[180],"MSE":[181],"0.120":[183],"0.041).":[185],"However,":[186],"its":[187],"advantage":[188],"over":[189],"Forest":[191],"XGBoost":[193,243],"not":[195,208],"statistically":[196],"significant,":[197],"indicating":[198],"should":[201],"be":[202],"interpreted":[203],"as":[204],"top\u2010tier":[206],"but":[207],"universally":[209],"dominant":[210],"classifier.":[211],"In":[212],"contrast,":[213],"BDE\u2010ANN":[214,270],"provided":[215],"recall":[218],"(0.985":[219],"0.030)":[221],"F1\u2010score":[223],"(0.842":[224],"0.052),":[226],"making":[227],"it":[228],"particularly":[229],"suitable":[230],"recall\u2010priority":[232],"screening":[233],"where":[234],"missed":[235],"fire\u2010prone":[236],"areas":[237],"highly":[239,245],"undesirable.":[240],"RF":[241],"offered":[244],"competitive":[246],"substantially":[249],"lower":[250],"computational":[251],"cost.":[252],"Overall,":[253],"findings":[255],"support":[256],"task\u2010oriented":[258],"WSM":[259],"framework":[260],"which":[262],"preferable":[265],"discrimination,":[269],"fire\u2010detection\u2010oriented":[272],"screening,":[273],"tree\u2010based":[275],"ensembles":[276],"rapid":[278],"baseline":[279],"deployment.":[280]},"counts_by_year":[],"updated_date":"2026-06-14T06:15:56.279521","created_date":"2026-06-14T00:00:00"}
