To　address　complex　flood　wave　propagation　problems　characterized　by　discontinuity　and　anisotropic　superposition,　Split　Coefficient-based　Physical　Informed　Neural　Network　(SC-PINN)　is　proposed.　The　Split　Coefficient　(SC)　strategy　is　employed　to　decompose　the　spatial　features　of　flood　waves　along　different　propagation　directions.　Spatial　derivatives,　matching　each　spatial　feature　component,　are　obtained　through　the　Taylor　series,　ensuring　that　each　component　contains　only　the　information　of　waves　propagating　in　a　single　positive　or　negative　direction.　This　approach　captures　flow　characteristics　in　each　direction,　thereby　reducing　the　spectral　bias　encountered　by　PINN　when　learning　complex　flow　regimes　during　flood　wave　propagation.　To　verify　the　effectiveness　and　accuracy,　the　proposed　SC-PINN　is　applied　to　three　classical　dam-break　scenarios.　Additionally,　an　investigation　is　conducted　into　why　the　SC　strategy　assists　PINN　in　improving　the　accuracy　of　flood　forecasting.　The　results　indicate　that　as　the　changing　rate　in　water　depth　increases,　the　flow　characteristics　of　asymmetric　propagation　and　superposition　become　more　pronounced,　which　leads　to　PINN　failing　to　capture　the　complex　flow　regime　effectively.　In　contrast,　the　proposed　SC-PINN　splits　the　total　changing　rate　in　water　depth　along　different　propagation　directions,　enabling　the　network　model　to　independently　learn　the　changing　rate　component　in　water　depth　in　each　direction.　Consequently,　the　new　method　accurately　captures　not　only　the　strong　discontinuity　regions　in　shallow　water　flow　but　also　the　phenomena　of　double　shock　system,　vortex,　and　wake　formed　by　the　interaction　between　flood　waves　and　obstacles.　Furthermore,　the　proposed　approach　successfully　describes　asymmetric　flow　around　the　dam　breach　and　local　high-water　levels　induced　by　irregular　breaches.　It　provides　a　potent　solution　for　addressing　complex　flood　wave　propagation　problems　characterized　by　discontinuity　and　anisotropic　superposition.　©　2025　Elsevier　B.V.