A novel approach for high-performance heat index forecasting for the hottest region in Thailand

Abstract

In Thailand, the hottest area is the northern inland plains located in the Northern Thailand. In summer, from the middle of February to the middle of May, the temperature may rises up to 40◦ or above due to the changing of northeast monsoon to southwest monsoon and the impact of relative humidity. Relative humidity is the major factor that makes people feel hotter than the actual temperature. If only the air temperature was noticed, it is possible to take risk of overheating and heat illness, especially heat stroke that can be deadly. The heat index has used as an effective warning measurement, this calculated by Steadman’s equation and yields the real feel of body. In order to prevent the heat illness, the predictive analytics such as time series forecasting should be applied. The regular series was constructed by several time points in consecutive daily heat index, the seasonal and cycle effects will be analyzed simultaneously. This scenario leads to the complicated time series model and may cause inaccuracy of forecasting. The proposed study modify the data structure as the series of specific date and time for thirty years, i.e., 1-April to 30-April at time 4.00 p.m., this reveals distinguished increasing trend from year by year. Three trend-focused forecasting model be applied, the two benchmarking models are Holt’s linear trend model and time series regression model, being compared with the proposed model called autocorelated-based decomposition. The forecasting results of Uttaradit and Chiang Mai provinces heat index in recent thirty years show that the proposed approach yields more accuracy than the benchmarks. For Uttaradit, the MAPE of the proposed model less than the others from 26.8% to 36.9%, and less than the others from 48.9% to 55.9% in RMSE. For Chiang Mai, the MAPE of the proposed model less than the others from 16.9% to 36.9%, and less than the others from 27.5% to 61.6% in RMSE.