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The novelty of this framework lies in the auto-determined hyperparameter configurations for each base model based on the Bayesian optimization technique, which guarantees the fast and easy implementation in various practical applications. Moreover, the ensemble modeling technique auto consolidates the predictive capability of each base model to generate the final optimized hybrid model, which offers a better prediction of the overall fracture pattern evolution, as demonstrated by a case study. The comparison of the different prediction strategies exhibits that the direct prediction is a better option than the recursive prediction, in particular for a longer prediction distance. The proposed approach may be applied in various sequential data predictions by adopting the adaptive prediction scheme.<\/jats:p>","DOI":"10.1007\/s00366-022-01756-w","type":"journal-article","created":{"date-parts":[[2022,11,8]],"date-time":"2022-11-08T21:02:48Z","timestamp":1667941368000},"page":"3353-3370","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["An auto-tuned hybrid deep learning approach for predicting fracture evolution"],"prefix":"10.1007","volume":"39","author":[{"given":"Sheng","family":"Jiang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zifeng","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6243-4304","authenticated-orcid":false,"given":"Luming","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,11,8]]},"reference":[{"key":"1756_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrmms.2021.104665","volume":"141","author":"A Abe","year":"2021","unstructured":"Abe A, Kim TW, Horne RN (2021) Laboratory hydraulic stimulation experiments to investigate the interaction between newly formed and preexisting fractures. 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