Hydrodynamic Modelling of The Mahakam River: From Survey to Validation
Bibliographic record
Abstract
The Mahakam River is the second-largest tidal river in Indonesia where tidal forces experiences significant tidal impacts. This research highlights the role of hydrodynamic modeling of tidal rivers in enhancing the effectiveness of regional planning for water resources management. This study is supported by surveys consisting of a Geodetic Network for uniformity of the datum plane (MSL), Hydrometry (Water levels and Discharge measurements), and Bathymetry (River Geometry) that were conducted during the wet season on spring tide and neap tide simultaneously along 100km. The impacts of tidal variations at the river mouth and the river discharge from upstream are the major driving forces for the hydrodynamic process besides the lateral discharge is also included in this model. Sobek software was used to perform 1D hydrodynamic modeling. The model was calibrated using data from the neap tide, and then the Manning’s roughness coefficient was simulated under spring tide conditions for validation by comparing the simulated discharge with observed values at BM 06. RMSE were calculated to assess the Model calibration by comparing water levels and three performance indices (R2, PBIAS and NSE) were used to validate discharge at BM 06. The results of model performance indicated a close agreement between the observed and simulated water levels over both calibration and validation periods. Hydrodynamic modelling helps identify and resolve technical challenges, enabling more effective planning for IWRM and regional planning.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".