Prediction of Streamflow in the Brahmani River using GEP, SVM, and MLR Models
Bibliographic record
Abstract
This study uses four modeling techniques, Gene Expression Programming (GEP), Support Vector Machine (SVM), and Multiple Linear Regression (MLR), to estimate streamflow in the Brahmani River in India. The objective is to develop accurate models that can predict streamflow based on two different hydroclimatic parameters and one river physical parameter. The study utilizes historical data of streamflow and corresponding hydroclimatic variables, including rainfall, temperature, and physical parameter river stage. The dataset is split into training and testing sets to facilitate the creation and validation of models. Statistical measures like Mean Square Error (MSE), Root Mean Square Error (RMSE), and coefficient of determination (R2) are utilized to assess the efficacy of the GEP, SVM, and MLR models in estimating streamflow. The results indicate that all the models can effectively estimate streamflow in the Brahmani River. On the other hand, the GEP model performs better than the MLR and SVM models. Its capacity to capture the intricate interactions between hydroclimatic parameters and streamflow is demonstrated by its lower error values and higher R2 values. The analysis of the models reveals that rainfall, temperature, and river stages can be significant predictors for streamflow estimation of the Brahmani River. These findings emphasize the importance of incorporating multiple hydroclimatic parameters to enhance the accuracy of streamflow predictions. The study also emphasizes the benefits of employing GEP as a modeling tool because of its capacity to handle complicated patterns and non-linear connections. Overall, this research provides valuable insights into streamflow estimation in the Brahmani River using GEP, SVM, and MLR models with different hydroclimatic parameters. The findings contribute to developing reliable tools for water resource management and hydrological forecasting in the region, facilitating informed decision-making based on accurate streamflow predictions.
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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) | 0.001 | 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 | 0.000 | 0.001 |
| 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 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".