Flood　susceptibility　assessment　is　the　premise　and　foundation　to　prevent　flood　disaster　events　effectively.　To　accurately　assess　urban　flood　susceptibility　(UFS),　this　study　first　analyzes　the　advantages　and　disadvantages　of　multi-layer　perceptron　(MLP),　and　light　gradient　boosting　machine　(LightGBM),　and　designs　a　new　integrated　machine　learning　method　by　combining　logistic　regression　(LR)　method,　i.e.,　LG-MLP-LR.　Then,　we　verify　the　performance　of　LG-MLP-LR　by　taking　the　flood　disaster　events　in　Fuzhou　from　2013　to　2016　as　example　and　analyze　the　contribution　of　flood　conditioning　factors　by　calculating　the　SHapley　Additive　exPlanations　values.　Finally,　the　assessment　results　are　compared　with　MLP,　LightGBM,　XG-MLP-LR,　and　CB-MLP-LR.　The　results　show　that　(1)　the　selected　flood　conditioning　factors　can　accurately　depict　the　UFS　of　the　study　area;　(2)　compared　with　MLP,　LightGBM,　XG-MLP-LR,　and　CB-MLP-LR,　the　assessment　results　by　LG-MLP-LR　have　higher　average　accuracy　(94.950%)　and　higher　average　AUC　(98.813%);　(3)　the　factors　affecting　the　occurrence　and　damage　degree　of　flood　disaster　events　in　Fuzhou　from　2013　to　2016　were　elevation,　topographic　wetness　index,　maximum　one-day　rainfall,　and　stream　power　index,　respectively.　This　study　provides　a　new　idea　and　method　for　the　effective　prevention　and　control　of　flood　disasters　in　cities.　©　The　Author(s),　under　exclusive　licence　to　Springer　Nature　Switzerland　AG　2024.