Flood　and　landslides　in　mountainous　cities　triggered　by　rainstorm　can　severely　impact　people＇s　lives,　property　and　socioeconomic　development.　The　pre-hazard　early　warning　system　are　crucial　to　the　disaster　prevention,　and　would　be　an　important　part　of　smart　city　planning.　This　paper　introduced　a　way　to　support　the　pre-hazard　identification　based　on　ground　water　level　change　fast　prediction,　which　is　the　key　factor　for　occurrence　of　the　rainstorm-induced　hazard.　Firstly,　the　remote　monitoring　stations　supplied　by　solar　power　are　established,　the　data　about　the　water　content　of　surface　soil　and　rainfall　were　real-time　collected　from　different　sensors.　By　introducing　the　sliding　windows　of　historical　data　and　for　prediction,　the　early　warning　system　are　effective　in　pre-hazard　identification　as　considering　the　vulnerable　environmental　factors　such　as　rainfall,　surface　runoff,　temperature,　sunshine,　vegetations,　soil　properties　and　structures.　Based　on　the　model　analysis　on　the　4982　timeseries　samples,　taking　the　sliding　window　T=7d　of　historical　data　and　sliding　window　G=7d　for　prediction　as　an　example,　the　root-mean-square-error　(RMSE)　of　the　predicted　result　of　the　model　reached　0.0812,　with　R2　up　to　0.9776.　Thus,　Our　study　aims　to　provide　a　strategic　way　in　quick　response　for　the　connection　of　pre-disaster　planning　and　urban　planning,　and　the　improvement　of　disaster　prevention,　mitigation　capacities,　increasing　the　resilience　of　mountainous　cities　and　their　inhabitants.