A Comparative Analysis of Machine Learning Models for Weather Type Classification
DOI:
https://doi.org/10.57152/predatecs.v4i1.2494Keywords:
Artificial Neural Network, Decision Tree, Random Forest, Support Vector Machine, Weather TypeAbstract
Accurate weather type classification plays an important role in supporting decision-making across various sectors, including transportation, agriculture, logistics, and disaster management. Although numerous studies have applied machine learning to weather prediction, most compare only a limited number of algorithms or evaluate them under a single experimental setting. This study addresses this gap by providing a comprehensive benchmark of five widely used classification algorithms Decision Tree, Random Forest, Support Vector Machine (SVM), Multilayer Perceptron (MLP), and Artificial Neural Network (ANN) using the publicly available Weather Type Classification dataset from Kaggle. These algorithms were selected because they represent interpretable, ensemble-based, kernel-based, and neural network approaches, enabling a comprehensive comparison of different machine learning paradigms. The dataset consists of structured meteorological attributes, including temperature, humidity, wind speed, precipitation, visibility, season, and location. Model performance was evaluated using three train-test split ratios (70/30, 80/20, and 90/10) and assessed through accuracy, precision, recall, confusion matrix, and ROC curve analyses. The experimental results show that ensemble-based and neural network models outperform single-tree approaches. Random Forest, MLP, and ANN demonstrate superior and more consistent performance across all data split ratios, with ROC-AUC values approaching 0.99. These findings establish a practical benchmark for selecting appropriate machine learning models for multi-class weather classification and demonstrate the effectiveness of ensemble and neural network approaches for developing reliable weather prediction systems using publicly available datasets.
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Copyright (c) 2026 Rapika Dahlan, Mutiara Maharani, Faizal Risqi Andika, Aisyah Salsabila Purba, Siti Khusnul Khotimah, Juana Barus
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