Rainfall Level Estimation Using FMCW Radar: A Data Driven Approach
Bibliographic record
Abstract
This paper proposes a data-driven method for estimating rainfall levels—categorized as no rain, light rain, medium rain, and heavy rain—using data collected from a Frequency-Modulated Continuous Wave (FMCW) radar. First, multiple sets of radar data under varying conditions were collected, and confirmed the impact of rainfall. Subsequently, denoising processes were applied to the radar data. The main contribution of this paper is the formulation and analysis of key features to measure the effect of rainfall on radar point cloud data, including the number of point clouds, density, average intensity, and root mean squared error (RMSE) of intensity. Then the feature is augmented for training. The data was split into training and validation sets, and a four-layer Back Propagation Neural Network (BPNN) was employed to train on the feature-extracted training set, optimizing the neural network parameters in the process. Experimental results are presented, including validation using the validation set, demonstrating the effectiveness of the proposed approach in rainfall level estimation.
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 | 0.000 | 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 | 0.000 | 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 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".