HydroFusionNet:面向西江水位预测的CNN-LSTM-自注意力网络及昇腾CANN适配（英文）

HydroFusionNet: CNN-LSTM-self-attention network for water level prediction in Xijiang River with Ascend CANN adaptation

摘要

【目的】准确的水位预测对防洪减灾、航运安全和水资源管理至关重要。传统水文模型往往难以捕捉水位波动中复杂的时空依赖关系和非线性特征。【方法】针对这些局限性，提出HydroFusionNet深度学习框架，通过并行处理CNN-LSTM模块与自注意力机制提升预测精度：CNN-LSTM模块提取局部时空特征，自注意力机制捕获全局上下文关联，最终融合二者输出结果。该框架基于昇腾CANN架构优化部署，利用高性能计算实现高效训练与预测。【结果】在西江流域数据集上的实验表明，HydroFusionNet的RMSE指标达0.394,R²达0.894,性能优于传统模型。昇腾CANN架构使其训练速度提升2.3倍，能耗降低17%,展现出显著计算优势。【结论】该模型能有效刻画复杂水文动态特征，为智能水文管理与灾害防控提供实时水位预测支持。

Abstract

[Objective] Accurate water level prediction is essential for flood control, navigation safety, and water resource management. Traditional hydrological models often fail to capture the complex spatiotemporal dependencies and nonlinear characteristics in water level fluctuations. [Methods] To address these limitations, the Hydro Fusion Net deep learning framework was proposed, integrating CNN-LSTM module and self-attention mechanism in a parallel architecture to improve prediction accuracy. The CNN-LSTM module extracted local spatiotemporal dependencies, while the self-attention mechanism captured global contextual relationships. The outputs of both modules were fused for final prediction. This framework was optimized for deployment on the Ascend CANN platform, leveraging high-performance computing to achieve efficient training and prediction.[Results] Experiments on the Xijiang River dataset showed that Hydro Fusion Net outperformed conventional models, achieving an RMSE of 0. 394 and an R² of 0. 894. Furthermore, with optimization on Ascend CANN, the training speed increased by 2. 3 times, and energy consumption reduced by 17%, indicating significant computational advantages. [Conclusion] The proposed framework effectively captures complex hydrological dynamics, providing real-time water level prediction support for intelligent hydrological management and disaster prevention.

关键词

水位预测 / 深度学习 / CNN-LSTM / 自注意力机制 / 昇腾CANN / 水文 / 防洪减灾 / 预测精度

Key words

water level prediction / deep learning / CNN-LSTM / self-attention mechanism / Ascend CANN / hydrological / flood control and disaster reduction / predictioin a ccuracy

基金资助

国家自然科学基金(62466051)

广西青年教师基础能力提升项目(2024KY0692)

自治区级大学生创新创业训练计划项目(S202411354227)