Flow Simulation Using 2D Hydrodynamic Model at the Palu Estuary Based on National DEM (DEMNAS) Source Data
Bibliographic record
Abstract
2D hydrodynamic simulation is very important to be performed to interpret the flow characteristics of a river segment. The success of this simulation is determined not only by the input boundary data, but also by the quality of the data used to create the geometry model, such as terrestrial survey data, digital elevation model (DEM) or data from other sources. This paper aims to assess the use of National DEM data (DEMNAS) as the basis for constructing a 2D geometry model for flow simulation in the downstream segment of the Palu River, Sulawesi, Indonesia. The simulation results using this DEM were compared with simulations based on geometry generated from terrestrial survey data. The hourly observation discharge data at Point P3 and tidal observation data at Point P1 in the period March 17th – 18th, 2021 were assigned as the inputs at the upstream and downstream boundaries, respectively. The performance of the two model scenarios was evaluated by comparing the water surface elevation observed and simulated during the time range at Point P2 using the efficiency of Nash–Sutcliffe (NSE). The simulation results show that the two geometry-forming data provide different performance against NSE. The terrestrial survey data shows a fairly good performance, while the DEMNAS data indicates a poor performance with a negative NSE. Based on the NSE of these two scenarios, it can be interpreted that the DEMNAS data is still not sufficient to construct the model geometry for the case in this area. This is not only related to the DEMNAS resolution, especially the vertical resolution, but is also related to the very low topographical slope in the estuary of the river. However, the use of these data in areas of higher slope can be re-evaluated.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".