用样条参数化网络轻量化预测台风，精度高且适合边缘设备部署。
KAN-FIF: Spline-Parameterized Lightweight Physics-based Tropical Cyclone Estimation on Meteorological Satellite
- 引入样条参数化的KAN层，捕捉台风属性间的高阶非线性关系。
- 相比基线模型参数减少94.8%，推理速度提升68.7%，MAE降低32.5%。
- 已在风云四号卫星处理器上实现14.41ms/样本延迟，适合实际业务应用。
热带气旋是破坏力极强的自然灾害，常引发强风、暴雨和风暴潮，对沿海地区造成巨大损失。及时监测对减灾至关重要，但现有方法在资源受限的边缘设备上面临计算效率低、参数量大的问题。当前基于物理的模型因线性特征交互，难以捕捉台风属性间的高阶多项式关系，导致模型臃肿、硬件不兼容。为此，本文提出基于柯尔莫哥洛夫-阿诺德网络的特征交互框架（KAN-FIF），融合MLP与CNN，并引入样条参数化KAN层，构建轻量级多模态架构。在最大持续风速（MSW）预测任务中，相较于基线模型Phy-CoCo，KAN-FIF参数量减少94.8%（0.99MB vs 19MB），单样本推理速度提升68.7%（2.3ms vs 7.35ms），且平均绝对误差（MAE）降低32.5%。在青云-1000开发板上对FY-4系列气象卫星处理器进行离线部署实验，实现每样本14.41ms的推理延迟，验证了其在实际台风监测中的可行性，并拓展了边缘智能应用的部署潜力。代码已开源：https://github.com/Jinglin-Zhang/KAN-FIF。
原文摘要 · Abstract (English)
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.
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