用多周期注意力提升洪水预测精度
APS-LSTM: Exploiting Multi-Periodicity and Diverse Spatial Dependencies for Flood Forecasting
- 通过FFT划分多周期，捕捉复杂时间模式
- 融合周期内与跨周期空间依赖，提升预测性能
- 适合灾害预警与水利管理领域应用
精准洪水预测对防灾减灾至关重要。水文数据具有高度非线性的时序特征，并包含降雨与流量之间的复杂空间关系。现有模型难以捕捉这些复杂的时序特性与空间依赖。本文提出基于LSTM的自适应周期与空间注意力方法（APS-LSTM），从多周期视角学习时间特征，并从不同周期划分中捕获多样化的空间依赖。APS-LSTM包含三个阶段：(i) 多周期划分，利用快速傅里叶变换（FFT）分解多种周期模式；(ii) 时空信息提取，执行周期内与跨周期的自注意力机制，聚焦于内部与跨周期的时序模式及空间依赖；(iii) 自适应聚合，依据振幅强度对各周期划分的计算结果进行加权整合。在两个真实数据集上的大量实验验证了该方法的优越性。
原文摘要 · Abstract (English)
Accurate flood prediction is crucial for disaster prevention and mitigation. Hydrological data exhibit highly nonlinear temporal patterns and encompass complex spatial relationships between rainfall and flow. Existing flood prediction models struggle to capture these intricate temporal features and spatial dependencies. This paper presents an adaptive periodic and spatial self-attention method based on LSTM (APS-LSTM) to address these challenges. The APS-LSTM learns temporal features from a multi-periodicity perspective and captures diverse spatial dependencies from different period divisions. The APS-LSTM consists of three main stages, (i) Multi-Period Division, that utilizes Fast Fourier Transform (FFT) to divide various periodic patterns; (ii) Spatio-Temporal Information Extraction, that performs periodic and spatial self-attention focusing on intra- and inter-periodic temporal patterns and spatial dependencies; (iii) Adaptive Aggregation, that relies on amplitude strength to aggregate the computational results from each periodic division. The abundant experiments on two real-world datasets demonstrate the superiority of APS-LSTM. The code is available: https://github.com/oopcmd/APS-LSTM.
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