Improving Stream Solute Predictions With a Modified LSTM Model Incorporating Solute Interdependences and Hysteresis Patterns
Abstract Surface runoff and infiltrated water en route to the stream interact with dynamic landscape properties, ranging from vegetation and microbial activities to soil and geological attributes. Stream solute concentrations are highly variable and interconnected due to these interactions, flow pat...
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| Format: | Article |
| Language: | English |
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Wiley
2025-03-01
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| Series: | Journal of Geophysical Research: Machine Learning and Computation |
| Online Access: | https://doi.org/10.1029/2024JH000383 |
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| author | Tarun Agrawal Allison Goodwell Praveen Kumar |
| author_facet | Tarun Agrawal Allison Goodwell Praveen Kumar |
| author_sort | Tarun Agrawal |
| collection | DOAJ |
| description | Abstract Surface runoff and infiltrated water en route to the stream interact with dynamic landscape properties, ranging from vegetation and microbial activities to soil and geological attributes. Stream solute concentrations are highly variable and interconnected due to these interactions, flow paths, and residence times, and often exhibit hysteresis with flow. Significant unknowns remain about how point measurements of stream solute chemistry reflect interdependent hydrobiogeochemical and physical processes, and how signatures are encapsulated as nonlinear dynamical relationships between variables. We take a Machine Learning (ML) approach to understand and capture these dynamical relationships and improve predictions of solutes at short and long time scales. We introduce a physical process‐based “flow‐gate” into an Long Short‐Term Memory (LSTM) model, which enables the model to learn hysteresis behaviors if they exist. Further, we use information‐theoretic metrics to detect how solutes are interdependent and iteratively select source solutes that best predict a given target solute concentration. The “flow‐gate LSTM” model improves model predictions (1%–32% decreases in RMSE) relative to the standard LSTM model for all nine solutes included in the study. The predictive improvements from the flow‐gate LSTM model highlight the importance of lagged concentration and discharge relationships for certain solutes. It also indicates a potential limitation in the traditional LSTM model approach since flow rates are always provided as input sources, but this information is not fully utilized. This work provides a starting point for a predictive understanding of geochemical interdependencies using machine‐learning approaches and highlights potential improvements in model architecture. |
| format | Article |
| id | doaj-art-6ff3b0f6d6c14f06a9ebac1d7dd4ebb0 |
| institution | OA Journals |
| issn | 2993-5210 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Geophysical Research: Machine Learning and Computation |
| spelling | doaj-art-6ff3b0f6d6c14f06a9ebac1d7dd4ebb02025-08-20T01:49:35ZengWileyJournal of Geophysical Research: Machine Learning and Computation2993-52102025-03-0121n/an/a10.1029/2024JH000383Improving Stream Solute Predictions With a Modified LSTM Model Incorporating Solute Interdependences and Hysteresis PatternsTarun Agrawal0Allison Goodwell1Praveen Kumar2Civil and Environmental Engineering University of Illinois at Urbana‐Champaign Champaign IL USAPrairie Research Institute University of Illinois at Urbana‐Champaign Champaign IL USACivil and Environmental Engineering University of Illinois at Urbana‐Champaign Champaign IL USAAbstract Surface runoff and infiltrated water en route to the stream interact with dynamic landscape properties, ranging from vegetation and microbial activities to soil and geological attributes. Stream solute concentrations are highly variable and interconnected due to these interactions, flow paths, and residence times, and often exhibit hysteresis with flow. Significant unknowns remain about how point measurements of stream solute chemistry reflect interdependent hydrobiogeochemical and physical processes, and how signatures are encapsulated as nonlinear dynamical relationships between variables. We take a Machine Learning (ML) approach to understand and capture these dynamical relationships and improve predictions of solutes at short and long time scales. We introduce a physical process‐based “flow‐gate” into an Long Short‐Term Memory (LSTM) model, which enables the model to learn hysteresis behaviors if they exist. Further, we use information‐theoretic metrics to detect how solutes are interdependent and iteratively select source solutes that best predict a given target solute concentration. The “flow‐gate LSTM” model improves model predictions (1%–32% decreases in RMSE) relative to the standard LSTM model for all nine solutes included in the study. The predictive improvements from the flow‐gate LSTM model highlight the importance of lagged concentration and discharge relationships for certain solutes. It also indicates a potential limitation in the traditional LSTM model approach since flow rates are always provided as input sources, but this information is not fully utilized. This work provides a starting point for a predictive understanding of geochemical interdependencies using machine‐learning approaches and highlights potential improvements in model architecture.https://doi.org/10.1029/2024JH000383 |
| spellingShingle | Tarun Agrawal Allison Goodwell Praveen Kumar Improving Stream Solute Predictions With a Modified LSTM Model Incorporating Solute Interdependences and Hysteresis Patterns Journal of Geophysical Research: Machine Learning and Computation |
| title | Improving Stream Solute Predictions With a Modified LSTM Model Incorporating Solute Interdependences and Hysteresis Patterns |
| title_full | Improving Stream Solute Predictions With a Modified LSTM Model Incorporating Solute Interdependences and Hysteresis Patterns |
| title_fullStr | Improving Stream Solute Predictions With a Modified LSTM Model Incorporating Solute Interdependences and Hysteresis Patterns |
| title_full_unstemmed | Improving Stream Solute Predictions With a Modified LSTM Model Incorporating Solute Interdependences and Hysteresis Patterns |
| title_short | Improving Stream Solute Predictions With a Modified LSTM Model Incorporating Solute Interdependences and Hysteresis Patterns |
| title_sort | improving stream solute predictions with a modified lstm model incorporating solute interdependences and hysteresis patterns |
| url | https://doi.org/10.1029/2024JH000383 |
| work_keys_str_mv | AT tarunagrawal improvingstreamsolutepredictionswithamodifiedlstmmodelincorporatingsoluteinterdependencesandhysteresispatterns AT allisongoodwell improvingstreamsolutepredictionswithamodifiedlstmmodelincorporatingsoluteinterdependencesandhysteresispatterns AT praveenkumar improvingstreamsolutepredictionswithamodifiedlstmmodelincorporatingsoluteinterdependencesandhysteresispatterns |