Enhancing relative humidity modelling using L2 regularization updates
Abstract This study explores L2 regularization to mitigate overfitting in artificial neural networks (ANNs), focusing on the regularization coefficient, Lambda, and its effect on data distribution and multi-layer perceptron (MLP) performance. Meteorological data from Tangier (1985–2022) with eight v...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-04-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-94356-9 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850042386362662912 |
|---|---|
| author | Abdellah Ben Yahia Iman Kadir Abdelaziz Abdallaoui Abdellah El-Hmaidi |
| author_facet | Abdellah Ben Yahia Iman Kadir Abdelaziz Abdallaoui Abdellah El-Hmaidi |
| author_sort | Abdellah Ben Yahia |
| collection | DOAJ |
| description | Abstract This study explores L2 regularization to mitigate overfitting in artificial neural networks (ANNs), focusing on the regularization coefficient, Lambda, and its effect on data distribution and multi-layer perceptron (MLP) performance. Meteorological data from Tangier (1985–2022) with eight variables influencing relative humidity were analyzed using principal component analysis (PCA) and self-organizing maps (SOM). PCA identifies key correlations, such as between total precipitation and relative humidity or vapor pressure and temperature, but struggles with non-linear relationships. SOM complements PCA by highlighting data structure nuances and detect complex correlations. L2 regularization, particularly with Lambda = 0.01, effectively reduces data complexity and dispersion, preventing overfitting while enhancing prediction accuracy. Adjusting Lambda during training optimizes weight biases in Kohonen and MLP networks, improving model performance and enabling precise relative humidity prediction. This strategy demonstrates the value of combining PCA, SOM, and L2 regularization for meteorological modelling. |
| format | Article |
| id | doaj-art-91a651cd9dae4ff7aedc83a53fe7aa1a |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-91a651cd9dae4ff7aedc83a53fe7aa1a2025-08-20T02:55:35ZengNature PortfolioScientific Reports2045-23222025-04-0115111410.1038/s41598-025-94356-9Enhancing relative humidity modelling using L2 regularization updatesAbdellah Ben Yahia0Iman Kadir1Abdelaziz Abdallaoui2Abdellah El-Hmaidi3Laboratory of Analytical Chemistry and Electrochemistry, Faculty of Sciences, Processes and Environment Team, URL-CNRST N 13, Moulay Ismail UniversityLaboratory of Analytical Chemistry and Electrochemistry, Faculty of Sciences, Processes and Environment Team, URL-CNRST N 13, Moulay Ismail UniversityLaboratory of Analytical Chemistry and Electrochemistry, Faculty of Sciences, Processes and Environment Team, URL-CNRST N 13, Moulay Ismail UniversityLaboratory of Analytical Chemistry and Electrochemistry, Faculty of Sciences, Processes and Environment Team, URL-CNRST N 13, Moulay Ismail UniversityAbstract This study explores L2 regularization to mitigate overfitting in artificial neural networks (ANNs), focusing on the regularization coefficient, Lambda, and its effect on data distribution and multi-layer perceptron (MLP) performance. Meteorological data from Tangier (1985–2022) with eight variables influencing relative humidity were analyzed using principal component analysis (PCA) and self-organizing maps (SOM). PCA identifies key correlations, such as between total precipitation and relative humidity or vapor pressure and temperature, but struggles with non-linear relationships. SOM complements PCA by highlighting data structure nuances and detect complex correlations. L2 regularization, particularly with Lambda = 0.01, effectively reduces data complexity and dispersion, preventing overfitting while enhancing prediction accuracy. Adjusting Lambda during training optimizes weight biases in Kohonen and MLP networks, improving model performance and enabling precise relative humidity prediction. This strategy demonstrates the value of combining PCA, SOM, and L2 regularization for meteorological modelling.https://doi.org/10.1038/s41598-025-94356-9OverfittingArtificial neural networksL2 regularizationRelative humidityAnd Tangier |
| spellingShingle | Abdellah Ben Yahia Iman Kadir Abdelaziz Abdallaoui Abdellah El-Hmaidi Enhancing relative humidity modelling using L2 regularization updates Scientific Reports Overfitting Artificial neural networks L2 regularization Relative humidity And Tangier |
| title | Enhancing relative humidity modelling using L2 regularization updates |
| title_full | Enhancing relative humidity modelling using L2 regularization updates |
| title_fullStr | Enhancing relative humidity modelling using L2 regularization updates |
| title_full_unstemmed | Enhancing relative humidity modelling using L2 regularization updates |
| title_short | Enhancing relative humidity modelling using L2 regularization updates |
| title_sort | enhancing relative humidity modelling using l2 regularization updates |
| topic | Overfitting Artificial neural networks L2 regularization Relative humidity And Tangier |
| url | https://doi.org/10.1038/s41598-025-94356-9 |
| work_keys_str_mv | AT abdellahbenyahia enhancingrelativehumiditymodellingusingl2regularizationupdates AT imankadir enhancingrelativehumiditymodellingusingl2regularizationupdates AT abdelazizabdallaoui enhancingrelativehumiditymodellingusingl2regularizationupdates AT abdellahelhmaidi enhancingrelativehumiditymodellingusingl2regularizationupdates |