Comparison of RNN-LSTM, TFDF and stacking model approach for weather forecasting in Bangladesh using historical data
Md Mahmudul Hasan1, Md Jahid Hasan1, Parisha Binte Rahman2
1Department of Mechanical and Production Engineering (MPE), Islamic University of Technology (IUT), Board Bazar, Gazipur, Bangladesh.
Plos One
|September 19, 2024
Summary
Accurate weather forecasting is vital for many sectors. This study shows that a stacking average model, using machine learning, significantly improves temperature predictions over other methods like TensorFlow Decision Forest and RNN-LSTM.
Area of Science:
- Meteorology
- Data Science
- Machine Learning
Background:
- Weather forecasting is complex due to non-linear systems and climate change.
- Accurate predictions are crucial for sectors like agriculture, transportation, and tourism.
- Traditional methods struggle with erratic weather patterns.
Purpose of the Study:
- To evaluate the effectiveness of data mining, meteorological analysis, and machine learning in enhancing weather forecast accuracy.
- To compare the performance of Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM), TensorFlow Decision Forest (TFDF), and model stacking techniques.
- To investigate the application of model stacking for mitigating overfitting and underfitting in weather prediction.
Main Methods:
- Utilized a 60-year meteorological dataset from Bangladesh, including rainfall, humidity, average temperature, and sea level pressure.
- Implemented and compared RNN-LSTM, TFDF, and a stacking model (ElasticNet, GradientBoost, KRR, Lasso) as base learners with a meta-model.
- Employed model stacking to aggregate predictions from multiple base models for improved accuracy and reliability.
Main Results:
- The stacking average model demonstrated superior performance in predicting average temperature compared to TFDF and RNN-LSTM.
- The stacking average model achieved a Root Mean Square Logarithmic Error (RMSLE) of 1.3002, a 10.906% improvement over the TFDF model.
- While the stacking average model excelled, individual stacking models showed less impressive performance, with TFDF yielding better validation results.
Conclusions:
- Model stacking, particularly the average approach, offers a significant advancement in weather forecasting accuracy for temperature prediction.
- The study highlights the potential of advanced machine learning techniques to address the challenges of forecasting in regions with unpredictable climate patterns.
- Further research into individual stacking model performance and validation strategies is warranted.
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