Ensemble and temporal feature-based framework for rainfall classification in Bangladesh
Mahir Shahriar Tamim, Md. Samiul Alim, Tanvir Ahmed Khan, Maisha Rahman, Md Musfique Anwar
TL;DR
This paper presents a machine learning framework for classifying daily rainfall in Bangladesh, using weather data to improve agriculture and disaster management.
Contribution
A novel ensemble and temporal feature-based machine learning framework for nationwide rainfall classification in Bangladesh.
Findings
Random Forest achieved the highest accuracy (77.37%) for rainfall classification.
Bi-LSTM performed best among deep learning models with 76.97% accuracy.
Humidity and sunshine duration were identified as the most influential predictors of rainfall intensity.
Abstract
Accurate rainfall classification is essential for Bangladesh, where monsoon variability strongly influences agriculture, water resource management, and disaster preparedness. This study proposes a robust machine learning framework for rainfall intensity classification at the daily temporal scale and nationwide spatial coverage, using over 543,839 daily weather records collected from 35 meteorological stations across several decades from a publicly available national meteorological dataset. The dataset includes rainfall, temperature, humidity, and sunshine duration, which were preprocessed and categorized into four intensity levels: No Rain, Light Rain, Moderate Rain, and Very Heavy Rain. Various models were evaluated, including Random Forest, Decision Trees, Gradient Boosting, K-Nearest Neighbors, Naïve Bayes, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine…
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Taxonomy
TopicsHydrological Forecasting Using AI · Precipitation Measurement and Analysis · Hydrology and Drought Analysis