Sensitivity analysis and optimization of land use/cover and aquifer parameters for improved calibration of hydrological model
Bibliographic record
Abstract
Integrated Flood Analysis System (IFAS) model, based on Tank model philosophy, is a widely used flood forecasting model that has the capability to simulate the catchment processes of any river system provided the surface and aquifer parameters of each sub-model are accurately calibrated. In this study, sensitivity analysis and optimization of hydrogeological parameters of Tank model have been performed to identify the key hydrogeological parameters and their significance in simulating the stream flows in the basins of two important rivers of Pakistan – Jhelum River and Chenab Rivers – respectively. IFAS includes a set of four sub-models namely: surface tank model, sub-surface tank model, aquifer tank model and river course model. Each of the sub-models simulates its own flow processes using surface/aquifer parameters. In this study, sensitivity analysis is performed to identify the parameters that significantly affect the model performance to simulate the flows in the river. Linear stochastic metamodels of Jhelum River and Chenab River Basins developed in this study played the role of metamodels or surrogate functions to determine the ranges of parameter values in different flow periods. The outcome demonstrates when the aquifer tank parameters values obtained from metamodels are applied, the simulation results in a nearly accurate calibration, which clearly indicates the efficiency of present methodology and the important role of hydrogeological parameters. Further, the analysis of the variability in the effectiveness of these parameters in different flow periods as well as for different catchments areas depicts spatial-temporal heterogeneous characteristics. This confirms that the analysis should be directed independently for each study basin because the results of sensitivity analysis are not transferable among catchments.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".