Improved streamflow prediction accuracy in Boreal climate watershed using a LSTM model: A comparative study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Streamflow plays a vital role in water resource management and environmental impact assessment. This study is a novel application of the Long Short-Term Memory (LSTM) model, a type of recurrent neural network, for real-time streamflow prediction in the Upper Humber River Watershed in western Newfoundland. It also compares the performance of the LSTM model with the physically based SWAT model. The LSTM model was optimized by tuning hyperparameters and adjusting the window size to balance capturing historical data and ensuring prediction stability. Using single input variables such as daily average temperature or precipitation, the LSTM achieved a high Nash-Sutcliffe Efficiency (NSE) of 0.95. In comparison, the results show that the LSTM model delivers a more competitive performance, achieving an NSE of 0.95 versus SWAT’s 0.77, and a percent bias (PBIAS) of 0.62 compared to SWAT’s 8.26. Unlike SWAT, the LSTM model does not overestimate high flows and excels in predicting low flows. Additionally, the LSTM successfully predicted daily streamflow using real-time data. Despite challenges in interpretability and generalizability, the LSTM model demonstrated strong performance, particularly during extreme events, making it a valuable tool for streamflow prediction in cold climates where accurate forecasts are crucial for effective water management. This study highlights the potential of the LSTM model’s application to water resource management.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it