PM 2.5 Problem in Chiang Mai, Thailand: The Application of Maximizing Expected Utility with Imbalanced Loss Functions

Abstract

© 2020, Springer Nature Switzerland AG. Over the past decade, PM 2.5 (particulate matters with diameters 2.5 µ or smaller) pollution has become a severe problem in Chiang Mai, Thailand. The problem occurs during the dry season from January to May. Undoubtedly, an efficient prediction model will significantly improve public safety and mitigate damage caused. Nonetheless, particular groups of people, especially ones who are vulnerable to the pollution, may prefer the prediction to be over-predicted rather than under-predicted. The aim of this research is to provide PM 2.5 density prediction models based on individual’s preference. This will overcome the limit of classical prediction models where the over-prediction and under-prediction ratio are symmetric. The predictions are done via the maximizing expected utility technique with imbalanced loss functions. The study area is Chiang Mai province, Thailand. The study period is the dry season (January to May) from 2016 to 2018. The hourly data is provided by the Pollution Control Department, Ministry of Natural Resource and Environment, Thailand. The study results show that the predictions based on the maximizing expected utility technique with imbalanced loss functions improves the over prediction ratio of the prediction.