Tem2-KAN:基于改进的Kolmogorov-Arnold网络的数据驱动时间温度预测。

IF 6.5
Yongxiang Lei, Bin Deng, Ziyang Wang
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引用次数: 0

摘要

准确的温度预报依赖于传统的气象参数,这些参数对于监测天气信息和指导预报工作至关重要。本研究引入了一种深度学习架构,通过改进的Kolmogorov-Arnold网络进行高精度气候温度预测,命名为Tem2-KAN。基于Kolmogorov-Arnold表示定理,Tem2-KAN探索用样条参数化的单变量函数取代神经网络中的传统线性权重,在保持内在可解释性的同时实现非线性气候模式的动态学习。该框架通过自适应激活函数独特地将多层感知器(mlp)的通用近似能力与物理上有意义的特征可视化相结合,解决了黑箱气候模型的关键局限性。建立了一个温度预测管道,该管道首先对来自英国监测站的原始气象数据进行预处理,然后训练Tem2-KAN将历史趋势映射为多水平预测。对真实世界气候数据集的严格评估表明,Tem2-KAN在利用较少可训练参数的同时实现最先进的预测精度的双重优势。此外,一项系统消融研究量化了tem2 - kan特异性关键超参数(样条阶k,网格分辨率网格)对预测性能的敏感性。最后,通过函数空间分析从理论上证明了Tem2-KAN的通用逼近能力,并在实践中验证了其可解释性和预测性能。这些创新将Tem2-KAN定位为气候信息学的范式转换工具,为气象学家提供高预测性能和对温度动态的机械洞察。该框架降低了超参数复杂性,进一步提高了其在业务预测系统中的可行性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Tem2-KAN: Data-driven temporal temperature prediction via an improved Kolmogorov-Arnold network.

Accurate temperature forecasting relies on traditional meteorological parameters that are essential for monitoring weather informatics and guiding forecasting efforts. This study introduces a deep learning architecture for high-precision climate temperature forecasting via an improved Kolmogorov-Arnold Networks, named Tem2-KAN. Grounded in the Kolmogorov-Arnold representation theorem, Tem2-KAN explores replacing conventional linear weights in neural networks with spline-parameterized univariate functions, enabling dynamic learning of nonlinear climate patterns while maintaining intrinsic interpretability. The proposed framework uniquely integrates the universal approximation capabilities of Multi-Layer Perceptrons (MLPs) with physically meaningful feature visualization through its adaptive activation functions, addressing critical limitations of black-box climate models. A temperature prediction pipeline is established that first preprocesses raw meteorological data from UK monitoring stations, then trains Tem2-KAN to map historical trends to multi-horizon forecasts. Rigorous evaluations on real-world climate datasets demonstrate Tem2-KAN's dual advantage achieving state-of-the-art prediction accuracy while utilizing fewer trainable parameters. In addition, a systematic ablation study quantifies the sensitivity of key Tem2-KAN-specific hyperparameters (spline order k, grid resolution grid) on forecasting performance. Finally, we theoretically prove Tem2-KAN's universal approximation capacity through function space analysis, and practically, we demonstrate its interpretability and prediction performance. These innovations position Tem2-KAN as a paradigm-shifting tool for climate informatics, offering meteorologists both high predictive performance and mechanistic insight into temperature dynamics. The framework's reduced hyperparameter complexity further enhances its viability for operational forecasting systems.

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