A Comparative Study of Machine Learning Models for Hourly Forecasting of Air Temperature and Relative Humidity
📰 ArXiv cs.AI
Comparing machine learning models for hourly forecasting of air temperature and relative humidity
Action Steps
- Collect and preprocess historical climate data
- Split data into training and testing sets
- Train and evaluate seven machine learning models: XGBoost, Random Forest, SVR, MLP, Decision Tree, LSTM, and CNN-LSTM
- Compare model performance using metrics such as mean absolute error and root mean squared error
Who Needs to Know This
Data scientists and researchers on a team can benefit from this study to improve their forecasting models, while urban planners can apply the findings for better management of cities
Key Insight
💡 XGBoost and LSTM models perform well for short-term forecasting of air temperature and relative humidity
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💡 Machine learning models compared for hourly air temperature and humidity forecasting
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