Comparison of Weather Classification Methods on Weather Images Using GLCM Features With Random Forest and CatBoost Algorithms

Muhammad  Noorhafizi; Triando Hamonangan  Saragih; Muhammad Itqan  Mazdadi; Muliadi Muliadi; Rudy  Herteno; Hasri Awal Akbar  Rozaq

doi:10.59395/ijadis.v7i1.1456

Authors

Muhammad Noorhafizi Faculty of Mathematics and Natural Science, Department of Computer Science, Lambung Mangkurat University, Kalimantan, Indonesia
Triando Hamonangan Saragih Faculty of Mathematics and Natural Science, Department of Computer Science, Lambung Mangkurat University, Kalimantan, Indonesia
Muhammad Itqan Mazdadi Faculty of Mathematics and Natural Science, Department of Computer Science, Lambung Mangkurat University, Kalimantan, Indonesia
Muliadi Muliadi Faculty of Mathematics and Natural Science, Department of Computer Science, Lambung Mangkurat University, Kalimantan, Indonesia
Rudy Herteno Faculty of Mathematics and Natural Science, Department of Computer Science, Lambung Mangkurat University, Kalimantan, Indonesia
Hasri Awal Akbar Rozaq Graduate School of Informatics, Department of Computer Science, Gazi University, Ankara

DOI:

https://doi.org/10.59395/ijadis.v7i1.1456

Keywords:

Weather Classification , Image Processing , GLCM , Random Forest , Catboost

Abstract

Weather image classification is an essential process for improving automated weather information systems. However, most existing studies rely on numerical meteorological data and rarely utilize the textural characteristics embedded in atmospheric imagery. This study addresses that limitation by applying the Gray Level Co-Occurrence Matrix (GLCM) for texture feature extraction combined with Random Forest (RF) and CatBoost algorithms for classification. The dataset, obtained from Kaggle, consists of 1,125 weather images categorized into four classes: cloudy, rain, shine, and sunrise. All images were uniformly normalized and augmented using four rotation angles (0°, 45°, 90°, 135°). GLCM features were extracted with a pixel distance of 1 and gray-level quantization of 8, generating four statistical attributes: contrast, correlation, energy, and homogeneity. Both algorithms were optimized through parameter tuning and evaluated using a 5-fold cross-validation scheme with an 80:20 split ratio. Results show that the Random Forest model (n_estimators = 100, max_depth = 10, random_state = 42) achieved the highest accuracy of 92.43% (±1.12), precision of 92.50%, recall of 92.43%, and F1-score of 92.42%. In comparison, CatBoost (iterations = 100, learning_rate = 0.1, depth = 6) achieved an accuracy of 68.88% (±2.31). The findings demonstrate that GLCM feature extraction combined with Random Forest offers superior stability and accuracy for weather image classification, providing a foundation for efficient and interpretable weather information systems.

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