Water level prediction using deep learning models: A case study of the Kien Giang River, Quang Binh Province
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Time‐series water level prediction during natural disasters, for example, typhoons and storms, is crucial for both flood control and prevention. Utilizing data‐driven models that harness deep learning (DL) techniques has emerged as an attractive and effective approach to water level prediction. This paper proposed an innovative data‐driven methodology using DL network architectures of Gated Recurrent Unit (GRU), Long Short‐Term Memory (LSTM), and Bidirectional Long‐Short Term Memory (Bi‐LSTM) to predict the water level at the Le Thuy station in the Kien Giang River. These models were implemented and validated based on hourly rainfall and water level observations at meteo‐hydrological stations. Three combinations of input variables with different time leads and time lags were established to evaluate the forecast capability of three proposed models by using five metrics, that is, R 2 , MAE, RMSE, Max Error Value, and Max Error Time. The results revealed that the LSTM model outperformed the Bi‐LSTM and GRU models, when water level and rainfall observations for one‐time lag at three stations were used to predict the water level at the Le Thuy station with 1‐h time lead, with the five metrics registering at 0.999; 3.6 cm; 2.6 cm; 12.9 cm; and −1 h, respectively.
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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