Spatial prediction of spring locations in data poor region of Central Himalayas
Bibliographic record
Abstract
This research explores the methods for understanding groundwater springs distribution and occurrence using Geographic Information System (GIS) and Machine Learning technique in data poor areas of the Central Himalayas.The objectives of this study are to analyse the distribution of natural springs, evaluate three random forest models for its predictability and establish a model for the prediction of occurrence of springs.This study evaluates the primary causal factors for occurrence of springs.The data used in this study consists of 20 parameters based on topography, geology, lithology, hydrology and land use as causal factors, whereas 621 spring location and discharge (n ¼ 621) measured during 2014-2016 and 815 non-spring locations (generated by GIS tool) use as supporting evidence to train (80%) and test (20%) the prediction model.Results show that the Bootstrap method is comparatively reliable (92% accuracy) over Boosted tree (64% accuracy) and Decision tree (74% accuracy) methods to classify and predict the occurrence of springs in the watershed.Bootstrap Forest shows the high Prediction rate for True Positive (82% actual spring predicted as a spring) and True Negative (89% actual non-spring predicted as non-spring), and the model seems consistent in both responses.This model was then applied to an independent dataset to predict spring location estimates with 75% accuracy.Therefore, spatial statistical methods prove efficient at predicting spring occurrence in data poor regions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".