The　displacement　at　various　measurement　points　is　a　critical　indicator　that　can　intuitively　reflect　the　operational　properties　of　a　dam.　It　is　important　to　analyse　displacement　monitoring　data　in　a　timely　manner　and　make　reliable　predictions　of　dam　safety.　This　paper　proposes　a　GPR-based　model　for　dam　displacement　forecasting.　The　input　variables　of　the　monitoring　model　consider　hydraulic　factors,　thermal　factors　and　irreversible　factors,　and　the　output　variables　are　the　observed　displacements　of　the　dam.　An　example　analysis　based　on　the　proposed　method　is　performed　on　a　prototype　gravity　dam,　and　the　performance　of　different　simple/combined　covariance　functions　is　investigated　to　obtain　the　optimal　choice.　Compared　to　multiple　linear　regression,　radial　basis　function　network　(RBFN)　and　support　vector　machine　(SVM)　methods,　the　results　indicate　that　the　GPR-based　model　with　a　combined　covariance　function　significantly　improves　the　prediction　accuracy.　The　proposed　model　can　effectively　overcome　the　over-learning　and　poor　robustness　issues　of　approaches　such　as　RBFN　and　SVM.　In　addition,　the　GPR-based　forecasting　model　has　the　advantages　of　simplicity　in　the　training　process　and　the　capacity　to　provide　a　probabilistic　output.