Application of Deep Learning Method for Daily Streamflow Time-Series Prediction: A Case Study of the Kowmung River at Cedar Ford, Australia
Bibliographic record
Abstract
Sustainable management of water supplies faces a comprehensive challenge due to global climate change. Improving forecasts of streamflow based on erratic precipitation is a significant activity nowadays. In recent years, the techniques of data-driven have been widely used in the hydrological parameter’s prediction especially streamflow. In the current research, a deep learning model namely Long Short-Term Memory (LSTM), and two conventional machine learning models namely, Random Forest (RF), and Tree Boost (TB) were used to predict the streamflow of the Kowmung river at Cedar Ford in Australia. Different scenarios proposed to determine the optimal combination of input predictor variables, and the input predictor variables were selected based on the auto-correlation function (ACF). Model output was evaluated using indices of the root mean square error (RMSE), and the Nash and Sutcliffe coefficient (NSE). The findings showed that the LSTM model outperformed RF and TB in predicting the streamflow with RMSE and NSE equal to 102.411, and 0.911 respectively. for the LSTM model. The proposed model could adopt by hydrologists to solve the problems associated with forecasting daily streamflow with high precision. This study may not be generalized because of the geographical condition and the nature of the data for each location.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".