Monitoring and Measuring Surface Water in Semi-Arid Environment Using Satellite Data: A Case Study of Karachi
Bibliographic record
Abstract
Water is a dynamic and precious resource for all living creatures. Its significance is imperative for different sectors of economy at global and regional level. Sustainable use of land resources such as water is vital to carryout fundamental chores and has become an important area of investigation in developing and developed countries. Pakistan is an under developing country with agro-based economy and it is among the top of those countries which are facing acute water scarcity. World Bank has projected that by 2025 severe food shortage could be caused in Pakistan due to water scarcity.Karachi is the most populous city of Pakistan with high consumption of water and food but semi arid climate conditions and drastic variability in rainfall pattern make it prone to desertification and drought. In Karachi Hydrological drought is closely associated with agricultural drought. Hence, monitoring, quantification and mapping of water is vital for better planning.This Study is aimed at monitoring spatio-temporal variation of surface water in Karachi using Geoinformatic techniques. For this purpose four satellite images of Landsat -7 ETM + were used. Through NDWI spatial distribution of water and its seasonal variation was observed and maps of water availability in each Union council of Karachi using software ArcMap 10.1 were also developed for the quick and better interpration. Use of modern state of the art Remote sensing data coupled with GIS for the monitoring of land resources has proved very significant for evaluating the potential of resources in different administrative units for planning and decision making.
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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 | 0.003 | 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 | 0.001 |
| 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".