Enhanced Rainfall Forecasting: A Comparative Study of LightGBM and XGBoost Model

Accurate rainfall forecasting is crucial for mitigating the impacts of extreme weather conditions,especially in cities like Chennai, which are prone to floods and waterlogging due to heavy monsoon rains.Machine learning techniques, particularly ensemble learning models like LightGBM (Light GradientBoosting Machine) and XGBoost (Extreme Gradient Boosting), have demonstrated significant potentialin improving rainfall prediction accuracy. A comparative analysis of LightGBM and XGBoost evaluatestheir performance in forecasting rainfall patterns in Chennai. Historical weather data, includingtemperature, humidity, pressure, and past rainfall records, serve as inputs for training and testing bothmodels to assess their effectiveness. Results indicate that while both models enhance predictive capabilitiescompared to traditional statistical approaches, one outperforms the other in terms of accuracy, computationalefficiency, and interpretability. The findings contribute to improving rainfall forecasting for better disasterpreparedness and water resource management in Chennai.