Localized Weather Prediction Using Kolmogorov-Arnold Network-Based Models and Deep RNNs

• URL: http://arxiv.org/abs/2505.22686v1
• Date: Tue, 27 May 2025 18:01:57 GMT
• Title: Localized Weather Prediction Using Kolmogorov-Arnold Network-Based Models and Deep RNNs
• Authors: Ange-Clement Akazan, Verlon Roel Mbingui, Gnankan Landry Regis N'guessan, Issa Karambal,
• Abstract summary: This study benchmarks deep recurrent neural networks such as $textttLSTM, GRU, BiLSTM, BiGRU$, and Kolmogorov-Arnold-based models $(texttKAN and textttTKAN)$ for daily forecasting of temperature, precipitation, and pressure in two tropical cities.<n>We introduce two customized variants of $ textttTKAN$ that replace its original $textttSiLU$ activation function with $ textttGeLU$ and
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Weather forecasting is crucial for managing risks and economic planning, particularly in tropical Africa, where extreme events severely impact livelihoods. Yet, existing forecasting methods often struggle with the region's complex, non-linear weather patterns. This study benchmarks deep recurrent neural networks such as $\texttt{LSTM, GRU, BiLSTM, BiGRU}$, and Kolmogorov-Arnold-based models $(\texttt{KAN} and \texttt{TKAN})$ for daily forecasting of temperature, precipitation, and pressure in two tropical cities: Abidjan, Cote d'Ivoire (Ivory Coast) and Kigali (Rwanda). We further introduce two customized variants of $ \texttt{TKAN}$ that replace its original $\texttt{SiLU}$ activation function with $ \texttt{GeLU}$ and \texttt{MiSH}, respectively. Using station-level meteorological data spanning from 2010 to 2024, we evaluate all the models on standard regression metrics. $\texttt{KAN}$ achieves temperature prediction ($R^2=0.9986$ in Abidjan, $0.9998$ in Kigali, $\texttt{MSE} < 0.0014~^\circ C ^2$), while $\texttt{TKAN}$ variants minimize absolute errors for precipitation forecasting in low-rainfall regimes. The customized $\texttt{TKAN}$ models demonstrate improvements over the standard $\texttt{TKAN}$ across both datasets. Classical \texttt{RNNs} remain highly competitive for atmospheric pressure ($R^2 \approx 0.83{-}0.86$), outperforming $\texttt{KAN}$-based models in this task. These results highlight the potential of spline-based neural architectures for efficient and data-efficient forecasting.

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