An adaptive neuro-fuzzy inference system for the post-calibration of weather radar rainfall estimation
Bibliographic record
Abstract
An Adaptive Neuro-Fuzzy Inference System, based on a jack-knife approach, is proposed for the post-calibration of weather radar rainfall estimation exploiting available raingauge observations. The methodology relies on the construction of a fuzzy inference system with three inputs (radar x coordinate, y coordinate and rainfall estimation at raingauge locations) and one output (raingauge observations). Subtractive clustering is used to generate the initial fuzzy inference system. Artificial neural network learning provides a fast way to automatically generate additional fuzzy rules and membership functions for the fuzzy inference system. Fuzzy logic enhances the generalisation of the artificial neural network system. In order to demonstrate the steps of the radar rainfall post-calibration using the Adaptive Neuro-Fuzzy Inference System, CAPPIs of one-hour rainfall accumulation and corresponding raingauge observations have been selected. Results show that the proposed approach looks for a response that is a compromise between radar rainfall estimations and raingauge observations and does not necessarily consider the raingauge observations as ground truth. The algorithm is very fast and can be implemented for real time post-calibration. This algorithm makes use of all available data—raingauge observations are usually scarce—for training and checking the neuro-fuzzy inference system. It also provides a degree of reliability of the post-calibration.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".