A perceptible stacking ensemble model for air temperature prediction in a tropical climate zone

Abstract

AbstractBangladesh is one of the world’s most susceptible countries to climate change. Global warming has significantly increased surface temperatures worldwide, including in Bangladesh. According to meteorological observations, the average temperature of the world has risen approximately 1.2 °C to 1.3 °C over the last century. Researchers and decision-makers have recently paid attention into the climate change studies. Climate models are used extensively throughout the nation in studies on global climate change to determine future estimates and uncertainties. This paper outlines a perceptible stacking ensemble learning model to estimate the temperature of a tropical region—Cox’s Bazar, Bangladesh. The next day’s temperature, maximum temperature, and minimum temperature are estimated based on the daily weather database collected from the weather station of Cox’s Bazar for a period of 20 years between 2001 and 2021. Five machine learning (ML) models, namely linear regression (LR), ridge, support vector regression (SVR), random forest (RF), and light gradient boosting machine (LGBM) are selected out of twelve ML models and combined to integrate the outputs of each model to attain the desired predictive performance. Different statistical schemes based on time-lag values play a significant role in the feature engineering stage. Evaluation metrics like mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and coefficient of determination (R2) are determined to compare the predictive performance of the models. The findings imply that the stacking approach presented in this paper prevails over the standalone models. Specifically, the study reached the highest attainable R2 values (0.925, 0.736, and 0.965) for forecasting temperature, maximum temperature, and minimum temperature. The statistical test and trend analysis provide additional evidence of the excellent performance of the suggested model.