沃巴什河流域极端水文事件的归纳预报
Inductive Predictions of Extreme Hydrologic Events in The Wabash River Watershed
摘要：
我们提出了一个机器学习方法来预测极端水文事件的时空变化的水文和气象数据。我们使用了时间步缩减技术来减少计算和内存需求，并训练了一个双向LSTM网络，根据沃巴什河流域80多年来观察和模拟的时间序列数据来预测土壤水分和水流。我们证明，我们的简单模型可以比GeoMAN等复杂的注意力网络更快地训练，而不会牺牲准确性。根据土壤水分和径流量的预测值，我们预测极端水文事件（如干旱）的发生和严重程度。我们还证明，极端事件可以预测在不同的地理位置从训练过程中观察到的位置。这种空间感应设置使我们能够使用我们用沃巴什盆地数据训练的模型来预测美国和世界其他地区的极端事件。
英文摘要：
We present a machine learning method to predict extreme hydrologic events from spatially and temporally varying hydrological and meteorological data. We used a timestep reduction technique to reduce the computational and memory requirements and trained a bidirection LSTM network to predict soil water and stream flow from time series data observed and simulated over eighty years in the Wabash River Watershed. We show that our simple model can be trained much faster than complex attention networks such as GeoMAN without sacrificing accuracy. Based on the predicted values of soil water and stream flow, we predict the occurrence and severity of extreme hydrologic events such as droughts. We also demonstrate that extreme events can be predicted in geographical locations separate from locations observed during the training process. This spatially-inductive setting enables us to predict extreme events in other areas in the US and other parts of the world using our model trained with the Wabash Basin data.
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