Morphometric analysis of Mogamureru river basin at the YSR Kadapa District, Andhra Pradesh, India using GIS and remote sensing
Bibliographic record
Abstract
Water availability and scarcity are impacted by geomorphological changes that occur within a catchment. As a result, determining the influence of geomorphological processes on the catchment’s hydrology requires a quantitative study of the catchment geometry. Approaches based on remote sensing (RS) and geographic information systems (GIS) have grown in popularity in recent years because they assist strategists and decision-makers in making accurate and effective choices and plans. For this research, the Mogamureru River basin was chosen. The study shows that GIS and RS data can be used to analyse and approximate the period and erosional operations’ risk in a Mogamureru river basin for better design and maintenance. The quickest and most economical method of displaying the hydrologic and physiographic features of the Mogamureru river basin is to assess the morphometric parameters. All of the following parameters such as: linear (7) areal (5), relief (5), and drainage texture (5) characteristics were obtained with hypsometric curve for the Mogamureru river basin utilizing the Shuttle Radar Topography Mission (SRTM) images. According to the linear parameter values, the majority of streams (82 %) belong to the first order, Statistical analysis shows that there is a good relationship between stream order and stream length, as well as stream order and stream number. The circularity ratio (0.55) depicts the young, elongated topography. The basin as a whole is in the mature stage of formation, with good to moderate potential, according to the hypsometric index (0.46). Due to land deterioration, Pulivendula and Vemula have had the most soil loss. This study concludes that morphometric analysis based on GIS & remote sensing techniques is a competent tool for hydrological studies. The research findings will help policymakers for integrated river basin management, agricultural development, and water management. In addition, researchers of morph hydrological, geological and climatological research will be beneficiary. • Morphometric metrics are crucial for understanding river basins, highlighting their morphological and physiographic features. • River basins impact socio-economic and ecological features, showing the link between natural features and human activities. • The study indicates an inverse relationship between stream order and water availability, noting water presence in most streams. • Geomorphology is explained through stream length, indicating lithologic and structural heterogeneity within the watershed. • Surface runoff and sediment conditions suggest steep slopes and a high sediment transport rate, indicating erosion potential.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".