A Comparison of Machine Learning and Deep Learning Methods for Temperatures Predictions on Java Island
DOI:
https://doi.org/10.15294/edukom.v12i1.23812Keywords:
Climate change, Deep Learning, Forecasting, Machine Learning, TemperaturesAbstract
Climate change is a global long-term change in temperatures and weather. Climate change is a worldwide issue that requires proper handling to reduce the negative impact on humans and the environment. Analyzing historical data is beneficial for studying climate change. Machine learning and deep learning methods are useful tools for data analysis. The goal of this paper is to find the best model for forecasting temperatures, a case study in Java Island. Java Island is the most densely island and the central economy and business in Indonesia. Climate change research in Java Island is important for sustainability. It runs several algorithms i.e., Gradient Boosting, AdaBoost, XGBoost, CatBoost, Light GBM, Random Forest, Support Vector Regression, Extreme Learning Machine, Long Short-Term Memory, Gated Recurrent Unit, Bidirectional Long Short-Term Memory, and Bidirectional Gated Recurrent Unit. The experiment uses a historical daily time series of temperatures from 1 January 1990 to 31 December 2024. In general, the experimental results show that Gradient Boosting produces the highest average coefficient of determination R2 scores of 0.34 and the lowest Mean Absolute Error scores of 0.69. Long Short-Term Memory and Gated Recurrent Units are the deep learning models that also work well for forecasting. According to the experimental results, in some cases, machine learning models outperform deep learning models and vice versa.
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