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An improved LSTM is developed to effectively capture the dynamic nature of fire evolution. The SSA is employed to optimize the parameters of the LSTM, with its global optimization capability enhanced through the incorporation of four meta\u2010heuristic optimization methods. To comprehensively evaluate the model's effectiveness, comparative experiments were conducted against the Support Vector Machine (SVM), Random Forest (RF), Gated Recurrent Unit (GRU), and Transformer models, demonstrating the superiority of the improved SSA\u2010LSTM in multiple evaluation metrics. The advanced SSA\u2010LSTM model is then used to predict fire severity based on fire intensity. The applicability and effectiveness of the proposed model are validated through a practical fire experiment. Comparative analysis with existing approaches indicates that the proposed model achieves an approximately 15% improvement in prediction accuracy. In addition, the model shows potential for broader applications in dynamic fire trend identification and critical point warning systems.<\/jats:p>","DOI":"10.1002\/qre.3784","type":"journal-article","created":{"date-parts":[[2025,4,19]],"date-time":"2025-04-19T14:25:36Z","timestamp":1745072736000},"page":"2264-2278","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Risk Prognosis and Fire Prediction of Urban Utility Tunnels Using a Hybrid SSA\u2010LSTM Method"],"prefix":"10.1002","volume":"41","author":[{"given":"Tianlong","family":"Xu","sequence":"first","affiliation":[{"name":"School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China  Chengdu Sichuan China"},{"name":"Center for System Reliability and Safety University of Electronic Science and Technology of China  Chengdu Sichuan China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China  Chengdu Sichuan China"},{"name":"Center for System Reliability and Safety University of Electronic Science and Technology of China  Chengdu Sichuan China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4162-6387","authenticated-orcid":false,"given":"Ying","family":"Zeng","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China  Chengdu Sichuan China"},{"name":"Center for System Reliability and Safety University of Electronic Science and Technology of China  Chengdu Sichuan China"},{"name":"China University of Petroleum\u2010Beijing at Karamay  Karamay Xinjiang China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,4,19]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.adhoc.2020.102409"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2017.12.034"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11277-021-09175-8"},{"key":"e_1_2_8_5_1","doi-asserted-by":"crossref","unstructured":"A. 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