用天气图像时序预测地下水位，无需水文实测数据。
Time Distributed Deep Learning Models for Purely Exogenous Forecasting: Application to Water Table Depth Prediction using Weather Image Time Series
- 用时间分布式卷积网络提取每帧天气图像特征，再结合LSTM或改进WaveNet建模时序关系。
- 在意大利格拉纳-马伊拉流域测试，模型相关系数和KGE指标显著优于传统方法。
- 特别适合缺乏水文监测数据但有高质量天气数据的地区使用。
地下水是水循环中至关重要的组成部分，准确预测其变化对可持续资源管理至关重要。深度学习模型在水文学中表现优异，尤其擅长处理空间分布数据（如栅格数据）。然而，在许多地区，水文观测数据难以定期获取，甚至滞后严重；而气象数据通常更易获得且质量更高。本文提出两种纯外生变量深度学习模型，仅利用天气图像时序数据，预测意大利皮埃蒙特大区格拉纳-马伊拉流域的地下水位。两个模型均采用时间分布式卷积神经网络（TDC）将每时刻的图像编码为向量表示：第一种为TDC-LSTM，通过LSTM层捕捉时序依赖；第二种为改进的TDC-UnPWaveNet，采用新型通道分布式层处理不同长度序列，可生成未来更短且整体前移的预测序列。实验表明，两种模型均表现优异，其中TDC-LSTM更侧重降低偏差，而TDC-UnPWaveNet则更注重提升时序动态相关性与综合评估指标KGE。
原文摘要 · Abstract (English)
Groundwater resources are one of the most relevant elements in the water cycle, therefore developing models to accurately predict them is a pivotal task in the sustainable resource management framework. Deep Learning (DL) models have been revealed to be very effective in hydrology, especially by feeding spatially distributed data (e.g. raster data). In many regions, hydrological measurements are difficult to obtain regularly or periodically in time, and in some cases, the last available data are not up to date. Reversely, weather data, which significantly impacts water resources, are usually more available and with higher quality. More specifically, we have proposed two different DL models to predict the water table depth in the Grana-Maira catchment (Piemonte, IT) using only exogenous weather image time series. To deal with the image time series, both models are made of a first Time Distributed Convolutional Neural Network (TDC) which encodes the image available at each time step into a vectorial representation. The first model, TDC-LSTM uses then a Sequential Module based on an LSTM layer to learn temporal relations and output the predictions. The second model, TDC-UnPWaveNet uses instead a new version of the WaveNet architecture, adapted here to output a sequence shorter and completely shifted in the future with respect to the input one. To this aim, and to deal with the different sequence lengths in the UnPWaveNet, we have designed a new Channel Distributed layer, that acts like a Time Distributed one but on the channel dimension, i.e. applying the same set of operations to each channel of the input. TDC-LSTM and TDC-UnPWaveNet have shown both remarkable results. However, the two models have focused on different learnable information: TDC-LSTM has focused more on lowering the bias, while TDC-UnPWaveNet has focused more on the temporal dynamics, maximizing correlation, and KGE.
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