Multilayer Perceptron-based Predictive Model for the Reconstruction of Missing Rainfall Data
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The quality and completeness of rainfall data is a critical aspect in time series analysis and for prediction of future water-related disasters. An accurate estimation of missing data is essential for better rainfall prediction results. Multilayer perceptron (MLP) neural networks have been applied to solve stochastic problems in data science. This study suggests a novel approach for estimating missing rainfall data using MLP neural networks based on three configurations that are represented by the monsoon season (MS), non-monsoon season, and non-seasonal variation. For this purpose, a mathematical model was created to analyze and predict the time series of daily rainfall data in Seoul, South Korea. Missing rainfall data were reconstructed using the rainfall data of the other five stations after removing rainfall data from station number two in three time periods. The results of this study indicate that the new architecture of the MLP can accurately predict the missing rainfall data, particularly in the MS configuration when using only the rainfall data obtained during the MS. The performance of the proposed model was tested using the following evaluation criteria: root mean square error, mean absolute error, correlation coefficient, mean absolute deviation, mean absolute percentage error, and standard deviation. The confusion matrix showed values of 89, 83, and 92% for accuracy, recall, and precision, respectively. This indicates that the proposed model can effectively perform rainfall data reconstruction and predict missing rainfall data accurately when the length of the statistical period is limited to the MS with a high volume of rainfall.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it