Title: Improving public health outcomes through accurate UV index forecasting: ARIMA and ANN approach in Songkhla Province

Authors: Korakot Wichitsa-nguan Jetwanna; Orathai Yongseng; Supanan Kongmee; Tanongsak Sukyareak; Wasun Bunyod; Chidchanok Choksuchat; Nuntouchaporn Prateepausanont; Thanathip Limna

Addresses: Division of Computational Science, Faculty of Science, Prince of Songkla University, Songkhla, 90110, Thailand; Air Pollution and Health Effect Research Center, Prince of Songkla University, Songkhla, 90110, Thailand ' Division of Computational Science, Faculty of Science, Prince of Songkla University, Songkhla, 90110, Thailand ' Division of Computational Science, Faculty of Science, Prince of Songkla University, Songkhla, 90110, Thailand ' College of Digital Science, Prince of Songkla University, Songkhla, 90110, Thailand; Air Pollution and Health Effect Research Center, Prince of Songkla University, Songkhla, 90110, Thailand ' College of Digital Science, Prince of Songkla University, Songkhla, 90110, Thailand; Air Pollution and Health Effect Research Center, Prince of Songkla University, Songkhla, 90110, Thailand ' Division of Computational Science, Faculty of Science, Prince of Songkla University, Songkhla, 90110, Thailand; Air Pollution and Health Effect Research Center, Prince of Songkla University, Songkhla, 90110, Thailand ' College of Digital Science, Prince of Songkla University, Songkhla, 90110, Thailand; Air Pollution and Health Effect Research Center, Prince of Songkla University, Songkhla, 90110, Thailand; Faculty of Business Administration, Rajamangala University of Technology Srivijaya, Songkhla, 90000, Thailand ' Department of Computer Engineering, Faculty of Engineering, Prince of Songkla University, Songkhla, 90110, Thailand; Air Pollution and Health Effect Research Center, Prince of Songkla University, Songkhla, 90110, Thailand

Abstract: This research forecasts the UV Index using five weather parameters: temperature, dew point, humidity, wind speed, and atmospheric pressure in Muang District, Songkhla Province, over a period of 1000 days (from March 6, 2021, to November 30, 2023). It employs a combined autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) model for prediction. The ARIMA model outputs were further used to forecast the UV index with ANN, yielding high accuracy. The dataset was processed to handle missing data using median values. Results showed that the ARIMA model had the MAPE of 0.04% to 26.49%, MAE of 0.3% to 4.3%, and RMSE of 0.4-5.4%. Meanwhile, the ANN model demonstrated an accuracy of 94.2%.

Keywords: UV index prediction; ARIMA; autoregressive integrated moving average; ANN; artificial neural networks; weather parameters; public health outcomes.

DOI: 10.1504/IJDATS.2025.148565

International Journal of Data Analysis Techniques and Strategies, 2025 Vol.17 No.3, pp.254 - 277

Received: 13 Jul 2024
Accepted: 07 Jan 2025
Published online: 12 Sep 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article