Climate Change Impact Modeling Using Machine Learning
Predictive Analytics for Environmental Policy and Urban Planning
Penulis: Prof. Dr. Mada Rahman, S.Kom., M.T., Dr. Fitri Handayani, S.Kom., M.T., Dr. Michael Schmidt, Ph.D.
Informasi Konferensi
Konferensi: International Conference on Computational Science and Its Applications (ICCSA)
Tanggal: 2024-12-02
Lokasi: Melbourne, Australia
Abstrak
Abstract
This paper develops machine learning models for climate change impact assessment and prediction. We integrate multiple data sources including satellite imagery, weather patterns, and socio-economic indicators to create comprehensive predictive models for environmental policy making.
Modeling Approach:
- Ensemble machine learning for climate prediction
- Spatial-temporal analysis of environmental data
- Uncertainty quantification in climate projections
- Policy impact assessment frameworks
Model validation shows 89% accuracy in predicting local climate impacts and provides actionable insights for urban adaptation planning.
Kata Kunci
climate change modeling, machine learning, environmental policy, predictive analytics, Springer