Utilizing　machine　learning　to　predict　the　uplift　of　shield　tunnel　linings　ahead　of　the　cutterhead　during　construction　enables　timely　adjustments　of　control　parameters,　mitigating　lining　uplift　issues.　Nevertheless,　existing　models　exhibit　limited　performance　in　long　sequence　time-series　forecasting　(LSTF)　and　face　challenges　in　accurately　predicting　the　uplift　of　multiple　lining　rings　ahead　of　the　shield　cutterhead.　Considering　the　impact　of　shield　control,　attitude　parameters,　and　geological　condition,　and　utilizing　the　Boruta　algorithm　to　determine　model　input　features,　a　shield　tunnel　segment　uplift　prediction　model　based　on　LightGBM-Informer　was　proposed.　This　model　incorporates　a　wavelet　transform　filter　and　a　complete　ensemble　empirical　mode　decomposition　with　adaptive　noise　(CEEMDAN)　method　to　eliminate　noise　in　time　series　data.　The　accuracy　and　applicability　of　the　proposed　model　were　validated　using　the　monitoring　data　from　subway　shield　tunnel　projects　in　Nanjing　and　Xiamen.　The　results　demonstrate　that　the　model　exhibits　enhanced　prediction　accuracy　in　comparison　to　other　models,　including　recurrent　neural　network　(RNN),　long　short-term　memory　(LSTM),　gated　recurrent　unit　(GRU),　and　Transformer.　Additionally,　it　demonstrates　robust　generalization　capabilities　across　diverse　geological　condition　datasets.　As　the　length　of　the　prediction　sequence　increases,　the　performance　advantages　of　the　model　become　more　pronounced,　accurately　predicting　the　uplift　of　1−2　rings　of　linings　ahead　of　the　shield　cutterhead.　Feature　importance　analysis　based　on　Shapley　additive　explanations　(SHAP)　method　indicates　that　earth　chamber　pressure　and　vertical　displacement　at　the　shield　head　and　tail　have　significant　impacts　on　lining　uplift.　The　model　provides　theoretical　guidance　for　intelligent　control　of　shield　tunnel　lining　construction　in　complex,　water-rich　environments.　©　2024　Biodiversity　Research　Center　Academia　Sinica.　All　rights　reserved.