ESTIMATION OF HIGH-RESOLUTION RAINFALL USING MICROWAVE LINKS DATA OF CELLULAR SYSTEM
Bibliographic record
Abstract
One cannot manage what one does not measure is an old adage that is valid for the rainfall in the irrigated irrigation system. Water resources management required the efficient measurement of all water resources management component. Water resources management components measurement is the responsibility of respective organization. Rainfall measurement is the responsibility of the Pakistan Meteorological Department using standard gauge system. Deficiency in the system is the low spatial and temporal resolution. This directed the water manager towards the high resolution of satellite system. Satellite resolution is still course than the available cellular towers system and it is unable to capture the high resolution spatio-temporal variation in the Rainfall. This challenge of high resolution was conquered using microwave signals of Telenor cellular communication system in the surrounded area of Water Management Research Centre, University of Agriculture, Faisalabad. Rainfall was estimated based on the attenuation in the microwave signal between receiver and transmitter of the link. Receiver and transmitter are two different antenna on the cellular tower. One receiver and one transmitter make the single link. High resolution data at 15-minute temporal and 1.5Km spatial of total 24 links was processed using the R language written code. Results presented that the average daily rainfall using cellular system was 14.5 mm, while the satellite derived rainfall from Tropical Rainfall Measuring Mission (TRMM) was found zero and UAF meteorological showed 21.3 mm. Further the temporal resolution was found finer from cellular system that rainfall was occurred at 22:15 to 22:45. The spatial variation in the rainfall between links was found with the minimum of less than 1mm and maximum of 42.9 mm. This state-of-the-art techniques helps the hydrologist for comprehensive analysis and management of the water resources.
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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 | 0.000 | 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 | 0.001 | 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".