Data-driven　models　can　achieve　high　accuracy　and　low　computational　cost　without　a　priori　knowledge　of　hydrological　system,　which　have　been　successfully　applied　in　streamflow　forecasting　for　decades.　However,　it　is　still　a　challenging　task　to　improve　the　performance　of　these　models,　especially　for　the　rivers　with　dramatic　flow　changes.　In　this　paper,　a　new　integrated　framework　for　daily　streamflow　forecasting　based　on　different　flow　regimes　is　developed.　The　framework　integrates　a　Fuzzy　C-means　(FCM)　clustering　for　streamflow　regime　identification,　a　partial　mutual　information　(PMI)　for　input　selection,　Deep　Belief　Networks　(DBN)　for　mapping　the　nonlinear　relationships　between　the　selected　inputs　and　streamflow　during　different　streamflow　dynamics　processes,　and　a　rigorous　validation　process　considering　structure　validity　to　interpret　the　physical　processes　simulated　using　the　DBN　models.　The　framework　was　applied　to　three　streamflow　stations　with　different　climate　conditions　in　USA,　and　the　results　show　that　the　framework　has　significantly　improved　modelling　performance　(approximately　12%)　compared　to　single　data-driven　models.　The　integration　of　data-driven　models　and　physical　process　classification　also　leads　to　improved　integration　of　physical　understanding　of　the　complex　characteristic　of　different　flow　regimes　into　the　modelling　process,　leading　to　overall　improved　confidence　in　the　developed　daily　streamflow　forecasting　models.