针对地下水位时间序列所表现出的非线性、多尺度波动以及长程依赖等复杂的特性,传统的预测模型通常难以取得理想的预测效果。为提升复杂地下水位时间序列的预测精度,提出了一个结合特定数据预处理与CNN-Informer混合模型的预测框架。该框架首先对数据进行数据清洗、小波去噪以及一种结合极值与数据波动性的自适应重采样算法处理,以此来优化原始的高频数据。紧接着,构建了CNN-Informer模型,运用CNN来提取局部特征,Informer编码器来捕捉长程依赖,并且通过加权融合机制来进行综合性的预测。同时使用江苏丹徒苏18井的实测数据进行验证,结果表明所提出的CNN-Informer模型在MAE、RMSE和NSE等关键指标方面,均优于LSTM、CNN以及单独Informer等基线模型,显示出它在多步预测工作当中具有高精度和良好的鲁棒性。

The accurate prediction of the groundwater level is of vital importance for water resource management and disaster prevention and control. In view of the complex characteristics of the groundwater level time series, such as nonlinearity, multi-scale fluctuations and long-range dependence, traditional prediction models often fail to achieve satisfactory results. To improve the prediction accuracy of complex groundwater level time series, this paper proposes a prediction framework that combines specific data preprocessing with the CNN-Informer hybrid model. This framework first performs data cleaning, wavelet denoising and an adaptive resampling algorithm combining extremum and data volatility of the data to optimize the original high-frequency data. Subsequently, the CNN-Informer model is constructed. Local features are extracted using CNN, long-range dependencies are captured by the Informer encoder, and comprehensive predictions are made through the weighted fusion mechanism. Based on the verification of the measured data from Well Su 18 in Dantu, Jiangsu Province, the proposed CNN-Informer model significantly outperforms baseline models such as LSTM, CNN and individual Informer in key indicators such as MAE, RMSE and NSE, demonstrating its high accuracy and robustness in multi-step prediction. This research provides an effective new approach for the precise prediction of groundwater levels.