The Classification of Ultraviolet Index Using Logistic Regression and Random Forest methods for Predicting Extreme Conditions

DOI

10.2991/978-94-6463-366-5_2How to use a DOI?

Keywords

Classification; Logistic Regression; Prediction; Random Forest; Ultraviolet

Abstract

The Ultraviolet (UV) index is one of the most important markers for estimating potential exposure to harmful sun radiation. For the purpose of managing public health and environmental monitoring, it is imperative to predict high UV levels accurately. A study was designed and analyzed that used methodologies of logistic regression (LR) and random forest (RF) to forecast extreme UV conditions based on the UV Index. This article presents the findings. This study used logistic regression and random forest algorithms to assess the classification accuracy of high UV scenarios. The historical UV index data was used to train and validate the categorization model. Data gathered in 2022 from radiometer-based UV A and UV B radiation measurements done in Palu City, Central Sulawesi. In this test, the training and testing data sets are randomly divided into 70% and 30% respectively. Accuracy and F1 score are used to assess the model. Based on these findings, while the random forest model achieved an accuracy of 0.997 and an F1 score of 0.947, the logistic regression model achieved an accuracy of 0.958 and an F1 score of 0.996. Logistic regression is better than random forest techniques. These results show that the Random Forest model has better predictive ability than the Logistic regression model in making predictions in extreme and non-extreme condition of ultraviolet index.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Alfin Syarifuddin Syahab
AU  - Galih Langit Pamungkas
AU  - Saif Akmal
PY  - 2024
DA  - 2024/02/02
TI  - The Classification of Ultraviolet Index Using Logistic Regression and Random Forest methods for Predicting Extreme Conditions
BT  - Proceedings of the 2023 1st International Conference on Advanced Informatics and Intelligent Information Systems (ICAI3S 2023)
PB  - Atlantis Press
SP  - 4
EP  - 14
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-366-5_2
DO  - 10.2991/978-94-6463-366-5_2
ID  - Syahab2024
ER  -