{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T20:28:53Z","timestamp":1783628933563,"version":"3.55.0"},"reference-count":73,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2027,1,1]],"date-time":"2027-01-01T00:00:00Z","timestamp":1798761600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100013804","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100013804","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Reliability Engineering &amp; System Safety"],"published-print":{"date-parts":[[2027,1]]},"DOI":"10.1016\/j.ress.2026.113094","type":"journal-article","created":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T23:38:33Z","timestamp":1782949113000},"page":"113094","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"P3","title":["Innovative machine learning-based prediction of time-to-failure and domino fire escalation in chemical storage tanks"],"prefix":"10.1016","volume":"277","author":[{"given":"Zeeshan","family":"Khan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5959-7081","authenticated-orcid":false,"given":"Long","family":"Ding","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Ji","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ress.2026.113094_bib0001","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1007\/s11668-019-00642-w","article-title":"Methodologies for assessing risks of accidents in chemical process industries","volume":"19","author":"Basheer","year":"2019","journal-title":"J Fail Anal Prev"},{"key":"10.1016\/j.ress.2026.113094_bib0002","series-title":"Safety considerations in the chemical process industries","first-page":"1805","author":"Murphy","year":"2017"},{"key":"10.1016\/j.ress.2026.113094_bib0003","doi-asserted-by":"crossref","DOI":"10.1016\/j.jlp.2025.105570","article-title":"Uncertainty assessment of improved multistate reliability in the sociotechnical systems based on the polymorphic fuzzy entropy fault tree analysis and triptych cost-benefit-safety analysis","volume":"94","author":"Daas","year":"2025","journal-title":"J Loss Prev Process Ind"},{"issue":"5","key":"10.1016\/j.ress.2026.113094_bib0004","first-page":"4222","article-title":"A review of hazard management in petroleum\/chemical facilities-fires and explosions","volume":"20","author":"Alenezi","year":"2022","journal-title":"NeuroQuantology"},{"key":"10.1016\/j.ress.2026.113094_bib0005","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0282657","article-title":"Risk assessment of methanol storage tank fire accident using hybrid FTA-SPA","volume":"18","author":"Ramezanifar","year":"2023","journal-title":"PLoS One"},{"key":"10.1016\/j.ress.2026.113094_bib0006","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1016\/j.psep.2024.05.132","article-title":"Enhancing emergency response planning for natech accidents in process operations using functional resonance analysis method (FRAM): a case of fuel storage tank farm","volume":"188","author":"Zheng","year":"2024","journal-title":"Process Saf Environ Prot"},{"key":"10.1016\/j.ress.2026.113094_bib0007","doi-asserted-by":"crossref","DOI":"10.1016\/j.ssci.2024.106530","article-title":"Investigating the relationship between human and organisational factors, maintenance, and accidents. The case of chemical process industry in South Africa","volume":"176","author":"Gonyora","year":"2024","journal-title":"Saf Sci"},{"key":"10.1016\/j.ress.2026.113094_bib0008","doi-asserted-by":"crossref","DOI":"10.1016\/j.jlp.2024.105252","article-title":"Seismic risk in the chemical process industry: a semi-quantitative methodology for critical equipment identification","volume":"88","author":"Novelli","year":"2024","journal-title":"J Loss Prev Process Ind"},{"key":"10.1016\/j.ress.2026.113094_bib0009","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijdrr.2019.101072","article-title":"Dealing with cascading multi-hazard risks in national risk assessment: the case of Natech accidents","volume":"35","author":"Girgin","year":"2019","journal-title":"Int J Disaster Risk Reduct"},{"key":"10.1016\/j.ress.2026.113094_bib0010","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.jlp.2005.05.015","article-title":"A study of storage tank accidents","volume":"19","author":"Chang","year":"2006","journal-title":"J Loss Prev Process Ind"},{"key":"10.1016\/j.ress.2026.113094_bib0011","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0331180","article-title":"Analysis and intelligent prediction of domino effect accidents in chemical storage tanks with a focus on accident chain length","volume":"20","author":"Qi","year":"2025","journal-title":"PLoS One"},{"key":"10.1016\/j.ress.2026.113094_bib0012","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1021\/acs.chas.5c00024","article-title":"Domino effect risk modeling and analysis of tank area accidents based on accident chain and multifactor coupling","volume":"32","author":"Li","year":"2025","journal-title":"ACS Chem Health Saf"},{"key":"10.1016\/j.ress.2026.113094_bib0013","doi-asserted-by":"crossref","DOI":"10.1016\/j.csite.2026.107921","article-title":"Failure probability model for LPG spherical tanks under multi-pool fire coupling","volume":"80","author":"Wang","year":"2026","journal-title":"Case Stud Therm Eng"},{"key":"10.1016\/j.ress.2026.113094_bib0014","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1016\/j.psep.2021.01.042","article-title":"Dynamic analysis for fire-induced domino effects in chemical process industries","volume":"148","author":"Huang","year":"2021","journal-title":"Process Saf Environ Prot"},{"key":"10.1016\/j.ress.2026.113094_bib0015","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1016\/j.jlp.2010.06.013","article-title":"Domino effect in process-industry accidents \u2013 An inventory of past events and identification of some patterns","volume":"24","author":"Abdolhamidzadeh","year":"2011","journal-title":"J Loss Prev Process Ind"},{"key":"10.1016\/j.ress.2026.113094_bib0016","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.psep.2018.03.008","article-title":"Texas LPG fire: domino effects triggered by natural hazards","volume":"116","author":"Naderpour","year":"2018","journal-title":"Process Saf Environ Prot"},{"issue":"222","key":"10.1016\/j.ress.2026.113094_bib0017","article-title":"The fire and explosion at Indian Oil Corporation, Jaipur\u2014a summary of events and outcomes","author":"Fishwick","year":"2011","journal-title":"Loss Prev Bull"},{"key":"10.1016\/j.ress.2026.113094_bib0018","series-title":"Storage tank fire at intercontinental terminals company","year":"2023"},{"key":"10.1016\/j.ress.2026.113094_bib0019","series-title":"Large oil storage tank fire possibly sparked by lightning now out","year":"2024"},{"key":"10.1016\/j.ress.2026.113094_bib0020","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1002\/prs.10458","article-title":"Storage tank fire accidents","volume":"30","author":"Zheng","year":"2011","journal-title":"Process Saf Prog"},{"key":"10.1016\/j.ress.2026.113094_bib0021","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1111\/j.1539-6924.2012.01854.x","article-title":"Domino Effect analysis using Bayesian networks","volume":"33","author":"Khakzad","year":"2013","journal-title":"Risk Anal"},{"key":"10.1016\/j.ress.2026.113094_bib0022","series-title":"IOP Conference Series: Earth and Environmental Science","article-title":"Fire risk assessment on Floating Storage Regasification Unit (FSRU)","volume":"649","author":"Zhao","year":"2021"},{"key":"10.1016\/j.ress.2026.113094_bib0023","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.ress.2017.06.004","article-title":"Application of dynamic Bayesian network to performance assessment of fire protection systems during domino effects","volume":"167","author":"Khakzad","year":"2017","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.113094_bib0024","doi-asserted-by":"crossref","first-page":"760","DOI":"10.1002\/prs.12643","article-title":"Numerical study of failure modes of hazardous material tanks exposed to fire accidents in the process industry","volume":"43","author":"Mo","year":"2024","journal-title":"Process Saf Prog"},{"key":"10.1016\/j.ress.2026.113094_bib0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.jlp.2022.104800","article-title":"Synergistic effects on the physical effects of explosions in multi-hazard coupling accidents in chemical industries","volume":"77","author":"He","year":"2022","journal-title":"J Loss Prev Process Ind"},{"key":"10.1016\/j.ress.2026.113094_bib0026","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2024.109974","article-title":"A neural network approach to predict the time-to-failure of atmospheric tanks exposed to external fire","volume":"245","author":"Tamascelli","year":"2024","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.113094_bib0027","doi-asserted-by":"crossref","DOI":"10.1016\/j.compchemeng.2023.108556","article-title":"Dynamic Domino Effect Assessment (D2EA) in tank farms using a machine learning-based approach","volume":"181","author":"Amin","year":"2024","journal-title":"Comput Chem Eng"},{"key":"10.1016\/j.ress.2026.113094_bib0028","doi-asserted-by":"crossref","DOI":"10.1016\/j.jlp.2025.105714","article-title":"Explosion induced domino effect assessment in the process industries: a machine learning approach to improve probit models","volume":"98","author":"Jiang","year":"2025","journal-title":"J Loss Prev Process Ind"},{"key":"10.1016\/j.ress.2026.113094_bib0029","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2022.108587","article-title":"Interpretable boosting tree ensemble method for multisource building fire loss prediction","volume":"225","author":"Wang","year":"2022","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.113094_bib0030","doi-asserted-by":"crossref","DOI":"10.1016\/j.autcon.2022.104574","article-title":"Predicting real-time deformation of structure in fire using machine learning with CFD and FEM","volume":"143","author":"Ye","year":"2022","journal-title":"Autom Constr"},{"key":"10.1016\/j.ress.2026.113094_bib0031","doi-asserted-by":"crossref","DOI":"10.1016\/j.buildenv.2021.108315","article-title":"Deep learning to replace, improve, or aid CFD analysis in built environment applications: a review","volume":"206","author":"Calzolari","year":"2021","journal-title":"Build Env"},{"key":"10.1016\/j.ress.2026.113094_bib0032","first-page":"13709","article-title":"Recent advances and applications of surrogate models for finite element method computations: a review","volume":"26","author":"Kudela","year":"2022","journal-title":"Soft Comput 2022 26:24"},{"key":"10.1016\/j.ress.2026.113094_bib0033","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2021.107530","article-title":"Machine learning for reliability engineering and safety applications: review of current status and future opportunities","volume":"211","author":"Xu","year":"2021","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.113094_bib0034","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1007\/978-981-19-1939-8_40","article-title":"CFD simulation approach to predict fire-influenced Domino accident propagation pattern","author":"Ahmed Malik","year":"2023","journal-title":"Lect Notes Mech Eng"},{"key":"10.1016\/j.ress.2026.113094_bib0035","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.ress.2015.02.010","article-title":"LNG pool fire simulation for domino effect analysis","volume":"143","author":"Masum Jujuly","year":"2015","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.113094_bib0036","doi-asserted-by":"crossref","first-page":"340","DOI":"10.1016\/j.psep.2021.10.021","article-title":"Consequence modeling and domino effects analysis of synergistic effect for pool fires based on computational fluid dynamic","volume":"156","author":"Li","year":"2021","journal-title":"Process Saf Environ Prot"},{"key":"10.1016\/j.ress.2026.113094_bib0037","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/j.psep.2021.08.020","article-title":"Improved probit models to assess equipment failure caused by domino effect accounting for dynamic and synergistic effects of multiple fires","volume":"154","author":"Zhou","year":"2021","journal-title":"Process Saf Environ Prot"},{"key":"10.1016\/j.ress.2026.113094_bib0038","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2020.107278","article-title":"Assessment of safety barrier performance in the mitigation of domino scenarios caused by Natech events","volume":"205","author":"Misuri","year":"2021","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.113094_bib0039","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1021\/acs.chas.0c00075","article-title":"Machine learning and deep learning in chemical health and safety: a systematic review of techniques and applications","volume":"27","author":"Jiao","year":"2020","journal-title":"ACS Chem Health Saf"},{"key":"10.1016\/j.ress.2026.113094_bib0040","doi-asserted-by":"crossref","DOI":"10.1063\/5.0097496","article-title":"Velocity reconstruction in puffing pool fires with physics-informed neural networks","volume":"34","author":"Sitte","year":"2022","journal-title":"Phys Fluids"},{"key":"10.1016\/j.ress.2026.113094_bib0041","article-title":"Hybrid particle swarm optimization and physics informed neural network algorithm for temperature field reconstruction in industrial factory fires","volume":"113","author":"Li","year":"2025","journal-title":"J Build Eng"},{"key":"10.1016\/j.ress.2026.113094_bib0042","doi-asserted-by":"crossref","DOI":"10.1016\/j.firesaf.2020.102991","article-title":"Using machine learning in physics-based simulation of fire","volume":"114","author":"Lattimer","year":"2020","journal-title":"Fire Saf J"},{"key":"10.1016\/j.ress.2026.113094_bib0043","doi-asserted-by":"crossref","DOI":"10.1016\/j.firesaf.2025.104379","article-title":"Predicting the transient burning of non-charring materials using physics-informed neural networks","volume":"153","author":"Mozafari Parsa","year":"2025","journal-title":"Fire Saf J"},{"key":"10.1016\/j.ress.2026.113094_bib0044","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1016\/j.psep.2023.02.082","article-title":"Uncertainties and their treatment in the quantitative risk assessment of domino effects: classification and review","volume":"172","author":"Xu","year":"2023","journal-title":"Process Saf Environ Prot"},{"key":"10.1016\/j.ress.2026.113094_bib0045","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2023.110796","article-title":"Uncertainty quantification in machine learning for engineering design and health prognostics: a tutorial","volume":"205","author":"Nemani","year":"2023","journal-title":"Mech Syst Signal Process"},{"key":"10.1016\/j.ress.2026.113094_bib0046","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1016\/bs.mcps.2021.05.011","article-title":"Uncertainty in domino effects analysis","volume":"5","author":"Kong","year":"2021","journal-title":"In Methods Chem Process Saf"},{"key":"10.1016\/j.ress.2026.113094_bib0047","first-page":"2022","article-title":"An exploratory study on uncertainty analysis in quantitative risk assessment of domino effects","author":"Xu","year":"2022","journal-title":"Chem Eng Trans"},{"key":"10.1016\/j.ress.2026.113094_bib0048","doi-asserted-by":"crossref","first-page":"856","DOI":"10.1016\/j.psep.2024.11.055","article-title":"Dynamic probabilistic risk assessment considering the domino effect in chemical parks based on Monte Carlo simulation","volume":"193","author":"Hu","year":"2025","journal-title":"Process Saf Environ Prot"},{"key":"10.1016\/j.ress.2026.113094_bib0049","doi-asserted-by":"crossref","DOI":"10.1016\/j.ssci.2020.104802","article-title":"Combining uncertainty reasoning and deterministic modeling for risk analysis of fire-induced domino effects","volume":"129","author":"Ding","year":"2020","journal-title":"Saf Sci"},{"key":"10.1016\/j.ress.2026.113094_bib0050","doi-asserted-by":"crossref","DOI":"10.1016\/j.ssci.2021.105285","article-title":"A novel fuzzy dynamic bayesian network for dynamic risk assessment and uncertainty propagation quantification in uncertainty environment","volume":"141","author":"Guo","year":"2021","journal-title":"Saf Sci"},{"key":"10.1016\/j.ress.2026.113094_bib0051","doi-asserted-by":"crossref","DOI":"10.1016\/j.ces.2022.118410","article-title":"Analysis of uncertainty propagation path of fire-induced domino effect based on an approach of layered fuzzy Petri nets","volume":"268","author":"Guo","year":"2023","journal-title":"Chem Eng Sci"},{"key":"10.1016\/j.ress.2026.113094_bib0052","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2024.110532","article-title":"A Monte Carlo-based modeling method for the spatial-temporal evolution process of multi-hazard and higher-order domino effect","volume":"253","author":"Ma","year":"2025","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.113094_bib0053","doi-asserted-by":"crossref","first-page":"1192","DOI":"10.1016\/j.psep.2024.02.057","article-title":"Optimizing safety barrier allocation to prevent domino effects in large-scale chemical clusters using graph theory and optimization algorithms","volume":"184","author":"Zhang","year":"2024","journal-title":"Process Saf Environ Prot"},{"key":"10.1016\/j.ress.2026.113094_bib0054","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1016\/j.ress.2015.02.007","article-title":"Application of dynamic Bayesian network to risk analysis of domino effects in chemical infrastructures","volume":"138","author":"Khakzad","year":"2015","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.113094_bib0055","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.ress.2019.04.041","article-title":"FSEM: an approach to model contribution of synergistic effect of fires for domino effects","volume":"189","author":"Ding","year":"2019","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.113094_bib0056","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1016\/j.procs.2023.10.040","article-title":"Area estimation of forest fires using TabNet with transformers","volume":"225","author":"De Zarz\u00e0","year":"2023","journal-title":"Procedia Comput Sci"},{"key":"10.1016\/j.ress.2026.113094_bib0057","doi-asserted-by":"crossref","unstructured":"Amin M.T., Scarponi G.E., Cozzani V., Khan F. Improved pool fire-initiated domino effect assessment in atmospheric tank farms using structural response. Reliab Eng Syst Safety. 2024;242:109751. https:\/\/doi.org\/10.1016\/J.RESS.2023.109751.","DOI":"10.1016\/j.ress.2023.109751"},{"key":"10.1016\/j.ress.2026.113094_bib0058","series-title":"Pressure-relieving and depressuring systems","first-page":"248","author":"Standard","year":"2014"},{"key":"10.1016\/j.ress.2026.113094_bib0059","first-page":"52","article-title":"Effect of data scaling methods on machine learning algorithms and model performance","volume":"9","author":"Ahsan","year":"2021","journal-title":"Technol (Basel)"},{"key":"10.1016\/j.ress.2026.113094_bib0060","doi-asserted-by":"crossref","first-page":"2784","DOI":"10.1080\/01431161.2018.1433343","article-title":"Implementation of machine-learning classification in remote sensing: an applied review","volume":"39","author":"Maxwell","year":"2018","journal-title":"Int J Remote Sens"},{"key":"10.1016\/j.ress.2026.113094_bib0061","first-page":"6638","article-title":"CatBoost: unbiased boosting with categorical features","author":"Prokhorenkova","year":"2018","journal-title":"Adv Neural Inf Process Syst"},{"key":"10.1016\/j.ress.2026.113094_bib0062","doi-asserted-by":"crossref","DOI":"10.1016\/j.oceaneng.2024.116915","article-title":"Artificial neural network-based multi-input multi-output model for short-term storm surge prediction on the southeast coast of China 2024","volume":"300","author":"Qin","year":"2024","journal-title":"Ocean Eng"},{"key":"10.1016\/j.ress.2026.113094_bib0063","article-title":"Random search for hyper-parameter optimization","author":"Bergstra","year":"2012","journal-title":"J Mach Learn Res"},{"key":"10.1016\/j.ress.2026.113094_bib0064","doi-asserted-by":"crossref","unstructured":"Hutter F., Kotthoff L., Vanschoren J. Automated Machine learning methods, systems, challenges. Cham: springer Nature. 2019:219. https:\/\/doi.org\/10.1007\/978-3-030-05318-5.","DOI":"10.1007\/978-3-030-05318-5"},{"key":"10.1016\/j.ress.2026.113094_bib0065","doi-asserted-by":"crossref","DOI":"10.1016\/j.artint.2025.104292","article-title":"TTVAE: transformer-based generative modeling for tabular data generation","volume":"340","author":"Wang","year":"2025","journal-title":"Artif Intell"},{"key":"10.1016\/j.ress.2026.113094_bib0066","doi-asserted-by":"crossref","first-page":"351","DOI":"10.4258\/hir.2016.22.4.351","article-title":"Book review: deep Learning","volume":"22","author":"Kim","year":"2016","journal-title":"Heal Inf Res"},{"key":"10.1016\/j.ress.2026.113094_bib0067","first-page":"1929","article-title":"Dropout a simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J Mach Learn Res"},{"key":"10.1016\/j.ress.2026.113094_bib0068","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"Lecun","year":"2015","journal-title":"Nature"},{"key":"10.1016\/j.ress.2026.113094_bib0069","first-page":"1525","article-title":"Root mean square error (RMSE) or mean absolute error (MAE)?","volume":"7","author":"Chai","year":"2014","journal-title":"Geosci Model Dev Discuss"},{"key":"10.1016\/j.ress.2026.113094_bib0070","series-title":"Applied linear statistical models","author":"Kutner","year":"2005"},{"key":"10.1016\/j.ress.2026.113094_bib0071","series-title":"An introduction to statistical learning with applications in r","first-page":"367","author":"James","year":"2021"},{"key":"10.1016\/j.ress.2026.113094_bib0072","article-title":"Machine learning hybrid dynamic best model selection algorithm for real-time fire prediction using IoT-enabled multi-sensor data in buildings","volume":"7","author":"Khan","year":"2026","journal-title":"J Saf Sci Resil"},{"key":"10.1016\/j.ress.2026.113094_bib0073","doi-asserted-by":"crossref","first-page":"1730","DOI":"10.1002\/jat.4803","article-title":"TabNet and TabTransformer: novel deep learning models for chemical toxicity prediction in comparison with machine learning","volume":"45","author":"Mustafa","year":"2025","journal-title":"J Appl Toxicol"}],"container-title":["Reliability Engineering &amp; System Safety"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0951832026009038?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0951832026009038?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T20:05:14Z","timestamp":1783627514000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0951832026009038"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2027,1]]},"references-count":73,"alternative-id":["S0951832026009038"],"URL":"https:\/\/doi.org\/10.1016\/j.ress.2026.113094","relation":{},"ISSN":["0951-8320"],"issn-type":[{"value":"0951-8320","type":"print"}],"subject":[],"published":{"date-parts":[[2027,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Innovative machine learning-based prediction of time-to-failure and domino fire escalation in chemical storage tanks","name":"articletitle","label":"Article Title"},{"value":"Reliability Engineering & System Safety","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ress.2026.113094","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"113094"}}