Tailings　ponds　are　a　major　hazard,　and　are　ranked　18th　in　the　risk　assessment　of　world　accident　hazards.　The　saturation　line　height　is　one　of　the　most　important　factors　that　affects　the　safety　of　tailings　ponds.　Due　to　the　extremely　complicated　seepage　boundary　conditions　of　tailings　ponds,　a　precise　calculation　method　is　urgently　needed　for　predicting　the　saturation　lines.　Therefore,　the　dynamic　model　should　be　investigated　to　evaluate　the　potential　for　dam　breakage.　In　this　paper,　based　on　an　analysis　of　tailings　ponds　in　various　regions,　we　use　the　long　short-Term　memory　(LSTM)　algorithm　to　predict　the　time-series　variation　of　the　saturation　line　height.　To　evaluate　and　validate　our　model,　we　compare　with　traditional　models.　The　results　demonstrate　that　the　deep　learning　method　significantly　outperforms　the　traditional　methods,　provides　a　new　strategy　and　has　significant　potential　for　tailings　ponds　safety　prediction.　©　2013　IEEE.