📅 30 June 2024
DOI: 10.62951/ijsme.v1i2.57
Machine Learning Approaches for Climate Change Prediction: A Comparative Study
International Journal of Science and Mathematics Education
Asosiasi Riset Ilmu Matematika dan Sains Indonesia (ARIMSI)
📄 Abstract
This research explores various machine learning approaches, including deep learning and ensemble methods, to predict climate change indicators. We focus on temperature and precipitation trends using large datasets spanning multiple decades. By comparing the performance of algorithms like CNN, RNN, and random forests, we identify the most accurate models for specific climate variables. Our findings demonstrate that ensemble models provide better accuracy and reliability, especially for temperature predictions.
🔖 Keywords
#Machine learning; climate change prediction; deep learning; ensemble methods; climate data analysis
ℹ️ Informasi Publikasi
Tanggal Publikasi
30 June 2024
Volume / Nomor / Tahun
Volume 1, Nomor 2, Tahun 2024
📝 HOW TO CITE
Ardea Dewantari Prasetya; Abdul Latif Rahman; Muhammad Indra Novanto, "Machine Learning Approaches for Climate Change Prediction: A Comparative Study," International Journal of Science and Mathematics Education, vol. 1, no. 2, Jun. 2024.
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