Invertibility aware Integration of Static and Time-series data: An application to Lake Temperature Modeling. (2022 SDM Best Paper Award)
Invertibility aware Integration of Static and Time-series data: An application to Lake Temperature Modeling. (2022 SDM Best Paper Award)
复制标题
静态和时间序列数据的可逆性感知集成：湖温建模的应用。
DOI：
10.1137/1.9781611977172.79
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Kumar V.
中科院分区：
文献类型：
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作者：
Tayal, K.;Jia X.;Ghosh R.;Willard J.;Read J.;Kumar V.
水温的准确预测是许多决策和法规的基础，直接影响水质、渔业产量和电力生产。由于静态和时间序列数据监测的不同湖泊系统的数据分布存在差异，在实践中建立准确的大尺度湖泊温度预测模型仍然具有挑战性。在本文中，为了应对上述挑战，我们提出了一种基于机器学习的新颖方法，将静态和时间序列数据集成到深度循环模型中，我们称之为可逆感知长短期记忆（IA-LSTM），并证明了其在预测湖泊温度方面的有效性。我们提出的方法集成了可逆网络和 LSTM 的组件，以更好地预测温度分布（正向建模）并推断静态特征（即逆向建模），最终可以在静态变量丢失时增强预测。我们评估了我们的方法来预测美国中西部 450 个湖泊的温度剖面，并报告在捕获数据异质性方面相对改进了 4%，同时在静态特征不可用时比基线预测高出 12%。
Accurate predictions of water temperature are the foundation for many decisions and regulations, with direct impacts on water quality, fishery yields, and power production. Building accurate broad-scale models for lake temperature prediction remains challenging in practice due to the variability in the data distribution across different lake systems monitored by static and time-series data. In this paper, to tackle the above challenges, we propose a novel machine learning based approach for integrating static and time-series data in deep recurrent models, which we call Invertibility-Aware-Long Short-Term Memory(IA-LSTM), and demonstrate its effectiveness in predicting lake temperature. Our proposed method integrates components of the Invertible Network and LSTM to better predict temperature profiles (forward modeling) and infer the static features (i.e., inverse modeling) that can eventually enhance the prediction when static variables are missing. We evaluate our method on predicting the temperature profile of 450 lakes in the Midwestern U.S. and report relative improvement of 4% to capture data heterogeneity and simultaneously outperform baseline predictions by 12% when static features are unavailable.