Multi-Weather Classification Using Machine Learning: A Comparative Study

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

This paper presents a reproducible machine learning pipeline for multi-weather classification and provides a systematic comparative study of various classical and deep learning classifiers. The study details the complete workflow, including dataset exploration, preprocessing, feature extraction, and model evaluation. We evaluate the performance of multiple models under identical conditions, reporting standard metrics such as accuracy, precision, recall, F1-score, and confusion matrices.The contributions of this work are as follows: • A reproducible end-to-end pipeline applied to a multi-weather dataset. • A side-by-side comparison of widely used classifiers, providing a robust baseline for weather condition recognition. • An exploratory analysis of the dataset, including class distribution and feature characteristics. • Openly available source code and notebooks to ensure full reproducibility of the results.This work serves as a solid foundation for future research and practical applications in environmental and weather-related computer vision tasks.