Effectiveness of three machine learning models for prediction of daily streamflow and uncertainty assessment
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
This study evaluates three Machine Learning (ML) models-Temporal Kolmogorov-Arnold Networks (TKAN), Long Short-Term Memory (LSTM), and Temporal Convolutional Networks (TCN)-focusing on their capabilities to improve prediction accuracy and efficiency in streamflow forecasting. We adopt a data-centric approach, utilizing large, validated datasets to train the models, and apply SHapley Additive exPlanations (SHAP) to enhance the interpretability and reliability of the ML models. The results show that TKAN outperforms LSTM but slightly lags behind TCN in streamflow forecasting. TKAN demonstrated strong alignment with observed statistical parameters, achieving a Mean Absolute Error (MAE) of 5.799 m³/s and a Nash-Sutcliffe Efficiency (NSE) of 0.958, compared to MAE and NSE values of 8.865 m³/s and 0.942 for LSTM, and 5.706 m³/s and 0.961 for TCN, respectively. Multi-step forecasting revealed TKAN's robust performance up to a three-day forecast horizon, with a slight decline in accuracy as the forecast period extended. Uncertainty analysis indicated reasonable variance levels, with a mean 3-day forecast uncertainty of 35.02% at a 95% confidence level for TKAN, compared to 39.95% for LSTM and 28.46% for TCN. For a 7-day forecast, TKAN showed a mean uncertainty of 40.97%, compared to 45.01% for LSTM and 36.22% for TCN. By enhancing model transparency and improving datasets, this study significantly advances the integration of machine learning into hydrological forecasting, offering robust methods for developing adaptive water management systems in response to changing climate conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 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 | 0.000 |
| 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