{"id":"https://openalex.org/W7125934582","doi":"https://doi.org/10.48550/arxiv.2601.18897","title":"Explainable Uncertainty Quantification for Wastewater Treatment Energy Prediction via Interval Type-2 Neuro-Fuzzy System","display_name":"Explainable Uncertainty Quantification for Wastewater Treatment Energy Prediction via Interval Type-2 Neuro-Fuzzy System","publication_year":2026,"publication_date":"2026-01-26","ids":{"openalex":"https://openalex.org/W7125934582","doi":"https://doi.org/10.48550/arxiv.2601.18897"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2601.18897","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5117540372","display_name":"Qusai Khaled","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Khaled, Qusai","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122980093","display_name":"Bahjat Mallak","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mallak, Bahjat","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122103524","display_name":"Uzay Kaymak","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kaymak, Uzay","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5056706542","display_name":"Laura Genga","orcid":"https://orcid.org/0000-0001-8746-8826"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Genga, Laura","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5117540372"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.4738999903202057,"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":0.4738999903202057,"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/T10820","display_name":"Fuzzy Logic and Control Systems","score":0.17090000212192535,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10564","display_name":"Wastewater Treatment and Nitrogen Removal","score":0.04659999907016754,"subfield":{"id":"https://openalex.org/subfields/2310","display_name":"Pollution"},"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/uncertainty-quantification","display_name":"Uncertainty quantification","score":0.6376000046730042},{"id":"https://openalex.org/keywords/prediction-interval","display_name":"Prediction interval","score":0.6060000061988831},{"id":"https://openalex.org/keywords/uncertainty-analysis","display_name":"Uncertainty analysis","score":0.5931000113487244},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5745000243186951},{"id":"https://openalex.org/keywords/interval","display_name":"Interval (graph theory)","score":0.4894999861717224},{"id":"https://openalex.org/keywords/confidence-interval","display_name":"Confidence interval","score":0.460099995136261},{"id":"https://openalex.org/keywords/point-estimation","display_name":"Point estimation","score":0.4311000108718872},{"id":"https://openalex.org/keywords/adaptive-neuro-fuzzy-inference-system","display_name":"Adaptive neuro fuzzy inference system","score":0.42149999737739563},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.4189999997615814},{"id":"https://openalex.org/keywords/sensitivity-analysis","display_name":"Sensitivity analysis","score":0.4106999933719635}],"concepts":[{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.6376000046730042},{"id":"https://openalex.org/C103402496","wikidata":"https://www.wikidata.org/wiki/Q1106171","display_name":"Prediction interval","level":2,"score":0.6060000061988831},{"id":"https://openalex.org/C177803969","wikidata":"https://www.wikidata.org/wiki/Q29205","display_name":"Uncertainty analysis","level":2,"score":0.5931000113487244},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5745000243186951},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.4894999861717224},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.47920000553131104},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.47130000591278076},{"id":"https://openalex.org/C44249647","wikidata":"https://www.wikidata.org/wiki/Q208498","display_name":"Confidence interval","level":2,"score":0.460099995136261},{"id":"https://openalex.org/C41426520","wikidata":"https://www.wikidata.org/wiki/Q1192065","display_name":"Point estimation","level":2,"score":0.4311000108718872},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4228000044822693},{"id":"https://openalex.org/C186108316","wikidata":"https://www.wikidata.org/wiki/Q352530","display_name":"Adaptive neuro fuzzy inference system","level":4,"score":0.42149999737739563},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.4189999997615814},{"id":"https://openalex.org/C176147448","wikidata":"https://www.wikidata.org/wiki/Q1889114","display_name":"Sensitivity analysis","level":3,"score":0.4106999933719635},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.40709999203681946},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3864000141620636},{"id":"https://openalex.org/C121117317","wikidata":"https://www.wikidata.org/wiki/Q4439221","display_name":"Credible interval","level":3,"score":0.37470000982284546},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.36390000581741333},{"id":"https://openalex.org/C2987376176","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy inference system","level":5,"score":0.358599990606308},{"id":"https://openalex.org/C2988105877","wikidata":"https://www.wikidata.org/wiki/Q1341685","display_name":"Inference system","level":5,"score":0.3483000099658966},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.3463999927043915},{"id":"https://openalex.org/C137209882","wikidata":"https://www.wikidata.org/wiki/Q1403517","display_name":"Measurement uncertainty","level":2,"score":0.3449000120162964},{"id":"https://openalex.org/C132943942","wikidata":"https://www.wikidata.org/wiki/Q2562511","display_name":"Footprint","level":2,"score":0.34299999475479126},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.32109999656677246},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.3109000027179718},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3107999861240387},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.304500013589859},{"id":"https://openalex.org/C205167067","wikidata":"https://www.wikidata.org/wiki/Q3300636","display_name":"Interval estimation","level":3,"score":0.30320000648498535},{"id":"https://openalex.org/C42011625","wikidata":"https://www.wikidata.org/wiki/Q1055058","display_name":"Fuzzy set","level":3,"score":0.2816999852657318},{"id":"https://openalex.org/C195975749","wikidata":"https://www.wikidata.org/wiki/Q1475705","display_name":"Fuzzy control system","level":3,"score":0.2815999984741211},{"id":"https://openalex.org/C2780049643","wikidata":"https://www.wikidata.org/wiki/Q5511139","display_name":"Fuzzy rule","level":4,"score":0.2809999883174896},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.26809999346733093},{"id":"https://openalex.org/C2986395286","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy inference","level":5,"score":0.2637999951839447},{"id":"https://openalex.org/C140529851","wikidata":"https://www.wikidata.org/wiki/Q5160083","display_name":"Confidence and prediction bands","level":3,"score":0.26170000433921814},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.25609999895095825},{"id":"https://openalex.org/C123614077","wikidata":"https://www.wikidata.org/wiki/Q1364905","display_name":"Propagation of uncertainty","level":2,"score":0.25200000405311584},{"id":"https://openalex.org/C917703","wikidata":"https://www.wikidata.org/wiki/Q7239668","display_name":"Predictive inference","level":5,"score":0.25130000710487366}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2601.18897","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2601.18897","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.18897","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2601.18897","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.4625844359397888}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Wastewater":[0],"treatment":[1],"plants":[2],"consume":[3],"1-3%":[4],"of":[5,71],"global":[6],"electricity,":[7],"making":[8],"accurate":[9],"energy":[10],"forecasting":[11],"critical":[12],"for":[13,31],"operational":[14,127],"optimization":[15],"and":[16,85,129],"sustainability.":[17],"While":[18],"machine":[19],"learning":[20],"models":[21],"provide":[22],"point":[23],"predictions,":[24],"they":[25],"lack":[26],"explainable":[27,118],"uncertainty":[28,65,72,119],"quantification":[29],"essential":[30],"risk-aware":[32],"decision-making":[33],"in":[34,82],"safety-critical":[35],"infrastructure.":[36],"This":[37],"study":[38],"develops":[39],"an":[40],"Interval":[41],"Type-2":[42],"Adaptive":[43],"Neuro-Fuzzy":[44],"Inference":[45],"System":[46],"(IT2-ANFIS)":[47],"that":[48,121],"generates":[49],"interpretable":[50],"prediction":[51,90,123],"intervals":[52,87],"through":[53],"fuzzy":[54],"rule":[55],"structures.":[56],"Unlike":[57],"black-box":[58],"probabilistic":[59],"methods,":[60],"the":[61],"proposed":[62],"framework":[63],"decomposes":[64],"across":[66,113],"three":[67],"levels:":[68],"feature-level,":[69],"footprint":[70],"identify":[73],"which":[74],"variables":[75],"introduce":[76],"ambiguity,":[77],"rule-level":[78],"analysis":[79],"reveals":[80],"confidence":[81,124],"local":[83],"models,":[84],"instance-level":[86],"quantify":[88],"overall":[89],"uncertainty.":[91],"Validated":[92],"on":[93],"Melbourne":[94],"Water's":[95],"Eastern":[96],"Treatment":[97],"Plant":[98],"dataset,":[99],"IT2-ANFIS":[100],"achieves":[101],"comparable":[102],"predictive":[103],"performance":[104],"to":[105,126],"first":[106],"order":[107],"ANFIS":[108],"with":[109],"substantially":[110],"reduced":[111],"variance":[112],"training":[114],"runs,":[115],"while":[116],"providing":[117],"estimates":[120],"link":[122],"directly":[125],"conditions":[128],"input":[130],"variables.":[131]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-01-29T00:00:00"}