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.
|Original language||English (US)|
|Title of host publication||Proceedings of the 2022 SIAM International Conference on Data Mining, SDM 2022|
|Publisher||Society for Industrial and Applied Mathematics Publications|
|Number of pages||9|
|State||Published - 2022|
|Event||2022 SIAM International Conference on Data Mining, SDM 2022 - Virtual, Online|
Duration: Apr 28 2022 → Apr 30 2022
|Name||Proceedings of the 2022 SIAM International Conference on Data Mining, SDM 2022|
|Conference||2022 SIAM International Conference on Data Mining, SDM 2022|
|Period||4/28/22 → 4/30/22|
Bibliographical noteFunding Information:
This work is supported by the NSF award 1934721 under the Harnessing the Data Revolution (HDR) programe. Additional support provided by Department of the Interior Midwest Climate Adaptation Science Center. Any use of trade, firm, or product names is for descriptive purposes only and does not imply
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