Applicability of HEC-RAS and Geospatial Tools for Inland Waterways Transportation Corridor: Case Study of Ganga Basin, Bihar, India
Bibliographic record
Abstract
Bihar has the largest networks of Rivers and drainage systems and most of them are perineal. Previously, north Bihar had active inland waterways transportation networks but these days the networks are not in use. Due to rapid urbanization and a hike in fuel prices, the transportation cost within the state has risen in the past few years. The demand for cheaper transportation systems is highly needed in these areas for goods transportation. Recently, the inland waterways authority of India has started a new shipping service in River Ganga only but a few more river networks can also be used for these kinds of activities. A study was conducted to find the inland waterways' potential for the development of a state river transportation corridor for North Bihar India. Various mathematical modeling tools such as HEC-RAS, HEC-HMS along with geospatial tools have been used for this study. The result obtained by the study indicates that the six more rivers of the states have the potential for inland waterways transportation along with suitable vessels for transportation. The methodology developed in the study is suitable for the development of waterways transportation corridors at various places. The study also emphasizes selecting the proper places for making the jetties which may be helpful for various activities such as disaster management, industrial fright services, logistic supply, etc.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".