EKLT: Kolmogorov-Arnold attention-driven LSTM with Transformer model for river water level prediction

作者：Dongfeng Ren, Qian Hu, Tengda Zhang · 发表于：Journal of Hydrology · 年份：2024 · DOI：10.1016/j.jhydrol.2024.132430 · 被引用次数：43 · 研究领域：Hydrological Forecasting Using AI、Hydrology and Watershed Management Studies、Flood Risk Assessment and Management

Water level prediction is crucial for water resource management and flood warnings. Hybrid LSTM-based methods are widely used but face the following challenges: (1) The attention mechanism based on the universal approximation theorem (UAT) is the mainstream method for improving performance, but its essence is infinite approximation of functions, and the accuracy is difficult to improve; (2) LSTM struggles with modeling complex, long-term dependencies. To address these problems, we apply empirical mode decomposition (EMD) and propose input-spatial attention based on the Kolmogorov-Arnold theorem (KAT) for precise water level feature representation. Cascaded LSTM and Transformer structures capture long-term dependencies. Finally, temporal attention is embedded to achieve accurate water level prediction. Experiments show the proposed method achieves RMSE, MAE, MAPE and R 2 values of 0.1870, 0.1328, 1.1228 and 0.9540 in the Liaohe River of Liaoning Province, China, and 0.3027, 0.1844, 4.6034 and 0.8659 in the Hunhe River, respectively, indicating its strong predictive performance.