Prioritization of Tlawng River Basin of Mizoram Based on Erodibility Through Morphometric Analysis using GIS Technique
Bibliographic record
Abstract
Prioritizing watersheds based on erodibility holds significant importance, particularly in regions like the Tlawng basin, where the landscape is characterized by undulating hills and varying slopes along with annual heavy monsoon rains. This study involved conducting morphometric analysis and prioritizing 23 sub-basins within the Tlawng basin in Mizoram, utilizing Geographical Information System (GIS) techniques. ArcGIS software was employed to determine some fundamental morphometric parameters within each sub-watershed such as its basin area, perimeter, length of the basin, stream number, stream order, and stream length. Furthermore, the morphometric parameters pertaining to aerial and relief features were computed utilizing a range of established formulae. The findings reveal that the basin is characterized by a 5th order stream, with stream order 1 being the most prevalent. Subsequently, the Principal Component Analysis (PCA) was performed using the Statistical Package for the Social Sciences (SPSS) software to identify the principal components for prioritizing the sub-watersheds. Three parameters, namely stream frequency, compactness constant, and relative relief were identified as the principal components. Based on their ranking values, the compound parameter was computed, which was then used to assign the final rank to each sub-watershed in terms of erodibility. According to the findings, Sub-watershed 3, which had the lowest compound parameter value at 3.333, was assigned the top rank of 1, indicating its highest level of priority. Conversely, Sub-watershed 9 was given the lowest rank of 23, owing to its relatively higher compound parameter value of 22.333. The findings suggest that soil conservation efforts should initially be targeted at sub-watersheds with higher rankings, as they are relatively more susceptible to erosion and its related risks, and then implemented in the remaining watershed in the order of their priority. This prioritized approach will ensure the effective management and mitigation of soil erosion issues within the watershed.
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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 | 0.001 | 0.002 |
| 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".