Hilly　and　mountainous　regions　are　extensively　distributed　along　the　southeast　coast　in　China,　where　various　geological　disasters　occur　frequently　under　the　influence　of　factors　such　as　typhoons　and　heavy　rains.　Debris　flow　disasters　triggered　by　heavy　rains　in　this　area　are　characterized　by　suddenness,　cluster　occurrence,　and　certain　destructiveness,　and　they　may　impose　an　impact　on　hydraulic　structures　and　even　cause　serious　hazards　such　as　flow　blockage　if　occurring　near　river　channels　or　valleys.　Because　of　the　high　uncertainty　in　their　process,　effective　dynamic　parameter　inversion　methods　are　of　great　significance　for　analyzing　the　characteristics　of　their　evolution　and　formulating　effective　disaster　mitigation　measures.　This　paper　develops　a　new　parameter　inversion　method　based　on　the　multi-output　support　vector　regression　(M-SVR)　sub-model,　taking　the　＂5·8　Taining　Debris　Flow＂　event　as　a　study　case.　First,　based　on　the　debris　flow　dynamics　calculation　model　Geoflow_SPH,　we　construct　a　parallel　calling　framework　to　numerically　simulate　the　triggering　feature　parameter　combinations－including　internal　friction　angle,　unit　weight,　and　average　source　thickness－and　generate　initial　training　samples　that　contain　input　parameters　and　motion　characteristics.　And　the　sample　set　(1000　groups)　is　divided　into　training　sets　and　test　sets　in　proportion,　and　the　M-SVR　sub-model　is　trained　using　grid　search　technology.　Then,　we　use　the　sub-model　to　predict　and　calculate　the　subdivided　inversion　calculation　sample　set　(8000　groups),　taking　as　the　benchmark　the　debris　flow　velocity　recorded　in　the　geological　survey　report　at　three　control　sections,　and　selecting　the　parameter　combination　with　the　minimum　mean　square　error　(MSE)　as　the　final　inversion　result　through　calculating　the　predictions.　Finally,　we　examine　the　role　of　the　drainage　screen　structure　in　obstructing　the　movement　of　debris　flow　and　controlling　the　range　of　impact.　The　research　results　help　clarify　the　evolution　of　rainstorm-type　debris　flow　disasters　and　lay　a　theoretical　basis　for　application　of　various　disaster　mitigation　measures.　©　2025　Tsinghua　University.　All　rights　reserved.