Water　quality　prediction　provides　timely　insights　for　addressing　potential　water　environmental　issues.　Transformer-based　models　have　been　widely　used　in　water　quality　prediction.　However,　the　following　challenges　exist:　1)　Noise　in　the　time　series　of　water　quality　causes　nonlinear　models　to　be　overfit;　2)　It　is　difficult　to　identify　temporal　correlations　in　complex　time　series　data;　and　3)　Information　utilization　is　limited　in　long-term　prediction.　This　work　introduces　a　large-scale　water　quality　prediction　model　named　SVD-Autoformer　to　address　them.　SVD-Autoformer　combines　a　Savitzky-Golay　(SG)　filter,　variational　mode　decomposition　(VMD),　an　auto-correlation　mechanism,　and　a　deep　decomposition　architecture,　which　is　achieved　in　the　renovation　of　the　transformer.　First,　the　SG　filter　removes　noise　while　retaining　valuable　data　features.　SVD-Autoformer　employs　the　SG　filter　as　a　data　preprocessing　tool　to　reduce　noise　and　prevent　nonlinear　models　from　overfitting.　Second,　VMD　extracts　major　modes　of　the　signals　and　their　respective　center　frequencies,　thus　providing　richer　features　for　the　prediction.　Third,　the　deep　decomposition　architecture　with　embedded　decomposition　modules　allows　for　gradual　decomposition　during　the　prediction　process.　SVD-Autoformer　employs　the　architecture　to　extract　more　predictable　components　from　complicated　water　quality　time　series　for　long-term　forecasting.　Finally,　SVD-Autoformer　applies　the　auto-correlation　mechanism　to　capture　the　temporal　dependence　and　enhance　information　utilization.　Numerous　experiments　are　conducted　and　the　results　demonstrate　that　SVD-Autoformer　provides　superior　prediction　accuracy　over　other　advanced　prediction　methods　with　real-world　datasets.