Investigations on Impact of Feature Normalization Techniques for Prediction of Hydro-Climatology Data Using Neural Network Backpropagation with Three Layer Hidden
Bibliographic record
Abstract
Data normalization techniques are a very important initial stage to be carried out in order to obtain a good predictive data approach. Many researchers get different prediction and error results in each use of these data normalization techniques. Thereby, in this article discusses the accuracy rate of the seven normalization techniques at the preprocessing stage in the Neural Network Backpropagation (NNBP) architecture including decimal scaling, Z-score, min-max (there are 6 types), sigmoid, tanh estimators, mean-MAD, and median-MAD. We used two data patterns: seasonal data (rainfall) and stationary data (air humidity) that taken over the past 10 years (at 10-day intervals). We use accuracy rate parameters including number of epochs, MAE, and MSE when conducting training, testing, and predictions. The results showed that the Z-score technique was very good for the normalization of rainfall data with epochs of 10, MAE of 0.051, and MSE of 0.004. In the case of air humidity data, mean-MAD and Z-score techniques can be recommended with the number of mean-MAD technique epochs of 8, MAE of 0.013, MSE of 0.0004, while the number of epochs of Z-score techniques of 7, MAE of 0.018, and MSE of 0.0006. Thus, we conclude that when other researchers predict seasonal data or stationary data can use the Z-score technique for data normalization.
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) | 0.000 | 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".