Tropical cyclones (TC) are among the most destructive natural disasters, causing catastrophic damage to coastal regions through extreme winds, heavy rainfall, and storm surges. Timely monitoring of tropical cyclones is crucial for reducing loss of life and property, yet it is hindered by the computational inefficiency and high parameter counts of existing methods on resource-constrained edge devices. Current physics-guided models suffer from linear feature interactions that fail to capture high-order polynomial relationships between TC attributes, leading to inflated model sizes and hardware incompatibility. To overcome these challenges, this study introduces the Kolmogorov-Arnold Network-based Feature Interaction Framework (KAN-FIF), a lightweight multimodal architecture that integrates MLP and CNN layers with spline-parameterized KAN layers. For Maximum Sustained Wind (MSW) prediction, experiments demonstrate that the KAN-FIF framework achieves a $94.8\%$ reduction in parameters (0.99MB vs 19MB) and $68.7\%$ faster inference per sample (2.3ms vs 7.35ms) compared to baseline model Phy-CoCo, while maintaining superior accuracy with $32.5\%$ lower MAE. The offline deployment experiment of the FY-4 series meteorological satellite processor on the Qingyun-1000 development board achieved a 14.41ms per-sample inference latency with the KAN-FIF framework, demonstrating promising feasibility for operational TC monitoring and extending deployability to edge-device AI applications. The code is released at https://github.com/Jinglin-Zhang/KAN-FIF.

翻译：热带气旋（TC）是最具破坏性的自然灾害之一，其通过极端风力、强降雨和风暴潮对沿海地区造成灾难性破坏。对热带气旋进行及时监测对于减少生命和财产损失至关重要，但现有方法在资源受限的边缘设备上存在计算效率低和参数量大的问题，阻碍了其应用。当前基于物理引导的模型受限于线性特征交互，无法捕捉热带气旋属性间的高阶多项式关系，导致模型规模膨胀和硬件不兼容。为克服这些挑战，本研究提出了基于Kolmogorov-Arnold网络的特征交互框架（KAN-FIF），这是一种轻量级多模态架构，它将MLP和CNN层与样条参数化的KAN层相结合。在最大持续风速（MSW）预测任务中，实验表明，与基线模型Phy-CoCo相比，KAN-FIF框架实现了参数量减少$94.8\%$（0.99MB对比19MB），单样本推理速度提升$68.7\%$（2.3ms对比7.35ms），同时保持了更高的精度，其平均绝对误差（MAE）降低了$32.5\%$。在青云-1000开发板上进行的FY-4系列气象卫星处理器离线部署实验中，KAN-FIF框架实现了14.41ms的单样本推理延迟，证明了其在业务化热带气旋监测中具有良好的可行性，并将可部署性扩展至边缘设备AI应用。代码发布于https://github.com/Jinglin-Zhang/KAN-FIF。