Hameed et al. (2025) Exploratory Analysis and Prediction of Weather Conditions: Leveraging Feature Engineering and Machine Learning Models for Accurate Forecasting

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Identification

• Journal: Springer Link (Chiba Institute of Technology)
• Year: 2025
• Date: 2025-12-19
• Authors: Hashim Hameed, Archana Pandita
• DOI: 10.1051/epjconf/202534305008/pdf

Research Groups

Not specified in the provided text.

Short Summary

This study develops and evaluates machine learning models for weather prediction, demonstrating that Support Vector Machines (SVM) and effective feature engineering significantly enhance short-term forecasting accuracy for various applications.

Objective

• To enhance weather forecasting capabilities and achieve accurate predictions for sectors like agriculture, transportation, and disaster preparedness using data visualization and machine learning models.

Study Configuration

• Spatial Scale: Local (specific scale not quantified).
• Temporal Scale: Short-term (specific duration not quantified).

Methodology and Data

• Models used: Decision Trees, Support Vector Machines (SVM).
• Data sources: Comprehensive weather dataset (variables: precipitation, temperature, wind speed).

Main Results

• Support Vector Machines (SVM) consistently outperform other machine learning models in weather classification.
• Feature engineering, particularly date-based features and interaction terms, is crucial for significantly improving model prediction accuracy.

Contributions

• Demonstrates the effectiveness of machine learning for enhancing weather prediction capabilities.
• Highlights the vital role of feature engineering and specific feature types (date-based, interaction terms) in improving weather classification performance.
• Addresses gaps by focusing on localized weather patterns and exploring the potential of machine learning in forecasting.

Funding

Not specified in the provided text.

Citation

@article{Hameed2025Exploratory,
  author = {Hameed, Hashim and Pandita, Archana},
  title = {Exploratory Analysis and Prediction of Weather Conditions: Leveraging Feature Engineering and Machine Learning Models for Accurate Forecasting},
  journal = {Springer Link (Chiba Institute of Technology)},
  year = {2025},
  doi = {10.1051/epjconf/202534305008/pdf},
  url = {https://doi.org/10.1051/epjconf/202534305008/pdf}
}