论文标题
使用长期短期记忆神经网络预测Playa淹没
Predicting Playa Inundation Using a Long Short-Term Memory Neural Network
论文作者
论文摘要
在大平原上，Playas是候鸟的关键湿地栖息地，也是农业重要的高原含水层的充电来源。临时湿地表现出复杂的水文学，通过当地的暴雨迅速填充，然后通过蒸发和地下水浸润干燥。使用长期的短期记忆（LSTM）神经网络来解释这些复杂的过程，我们在1984 - 2018年的大平原上为71,842 Playas建模了Playa淹没。在单个PLAAS的级别上，该模型在固定测试集上达到了0.538的F1得分，显示了预测复杂淹没模式的能力。当在整个地区的所有Playas上取平均值时，即使在干旱期，该模型也能够非常紧密地跟踪淹没趋势。我们的结果证明了使用LSTM对复杂的水文动力学建模的潜力。我们的建模方法可用于在不同的气候场景下为未来建模Playa淹没，以更好地了解湿地栖息地和地下水将如何受到气候变化的影响。
In the Great Plains, playas are critical wetland habitats for migratory birds and a source of recharge for the agriculturally-important High Plains aquifer. The temporary wetlands exhibit complex hydrology, filling rapidly via local rain storms and then drying through evaporation and groundwater infiltration. Using a long short-term memory (LSTM) neural network to account for these complex processes, we modeled playa inundation for 71,842 playas in the Great Plains from 1984-2018. At the level of individual playas, the model achieved an F1-score of 0.538 on a withheld test set, displaying the ability to predict complex inundation patterns. When averaging over all the playas in the entire region, the model is able to very closely track inundation trends, even during periods of drought. Our results demonstrate potential for using LSTMs to model complex hydrological dynamics. Our modeling approach could be used to model playa inundation into the future under different climate scenarios to better understand how wetland habitats and groundwater will be impacted by changing climate.
