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

Korakot Wichitsa-nguan Jetwanna, Orathai Yongseng, Supanan Kongmee, Tanongsak Sukyareak, Wasun Bunyod, Chidchanok Choksuchat, Nuntouchaporn Prateepausanont and Thanathip Limna

International Journal of Data Analysis Techniques and Strategies, 2025, vol. 17, issue 3, 254-277

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. (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:ids:injdan:v:17:y:2025:i:3:p:254-277

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