In　this　paper,　a　machine-learning　architecture　with　the　integration　of　two　strategies　including　data　enhancement　and　adaptive　generation　scheme　for　Impact　Localization　(IL)　are　developed　to　address　the　aforementioned　issues　for　location　identification　of　impacts　on　composite　laminates.　Two　main　contributions　are　included　in　this　research:　First,　response　signals　collected　from　low-speed　impact　experiments　under　various　working　conditions　are　denoised　using　Adaptive　Sparse　Noise　Reduction　Algorithm　(ASNRA),　which　aims　at　maximizing　the　preservation　of　the　original　signal　amplitude,　thereby　avoiding　the　underestimation　of　pulse　features　during　denoising.　Then　a　RIME-optimized　Dual-layer　Support　Vector　Regression　(RDSVR)　method　for　the　real-time　update　of　hyperparameters　is　implemented　in　the　machine-learning　architecture　to　realize　IL.　The　superior　performances　of　the　IL　architecture　over　different　IL　models　are　validated　throughout　the　numerical　examples　in　terms　of　stability　and　efficiency.　Results　demonstrate　that　proposed　architecture　has　the　ability　to　realize　the　accurate　and　robust　IL　of　composite　laminates.