Invertibility aware Integration of Static and Time-series data: An application to Lake Temperature Modeling

Kshitij Tayal, Xiaowei Jia, Rahul Ghosh, Jared D Willard, Jordan Read, Vipin Kumar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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 languageEnglish (US)
Title of host publicationProceedings of the 2022 SIAM International Conference on Data Mining, SDM 2022
PublisherSociety for Industrial and Applied Mathematics Publications
Pages702-710
Number of pages9
ISBN (Electronic)9781611977172
StatePublished - 2022
Event2022 SIAM International Conference on Data Mining, SDM 2022 - Virtual, Online
Duration: Apr 28 2022Apr 30 2022

Publication series

NameProceedings of the 2022 SIAM International Conference on Data Mining, SDM 2022

Conference

Conference2022 SIAM International Conference on Data Mining, SDM 2022
CityVirtual, Online
Period4/28/224/30/22

Bibliographical note

Funding 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

Publisher Copyright:
Copyright © 2022 by SIAM.

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