Work

District-Level Rainfall and Cloudburst Prediction Using XGBoost: A Machine Learning Approach for Early Warning Systems

Year: 2025

Type: article

Abstract: This research presents a novel methodology for cloudburst forecasting using the XGBoost (Extreme Gradient Boosting) machine learning algorithm. With the escalating impact of climate change, accurately predicting extreme weather events like cloudbursts is crucial due to their potential to trigger floods. Cloudburst events were identified from daily rainfall data. Our study leverages historical weather data, focusing on intricate rainfall patterns, to forecast future cloudburst occurrences. Comparative analysis against Random Forest and LSTM models confirmed XGBoost’s effectiveness, consistently outperforming alternatives across multiple performance metrics. The XGBoost model, known for its ability to handle complex datasets, demonstrated strong predictive performance, with an RMSE of 0.12 and an MAE of 0.09, indicating high accuracy. This research provides a reliable tool for advanced weather forecasting and early warning systems, offering valuable support to policymakers, disaster management teams, and agricultural planners in mitigating risks associated with extreme rainfall events.

Source: Informatica

DOI: 10.31449/inf.v49i2.7612

Authors: Guru Dayal Kumar, Shekhar Tyagi, Kalandi Charan Pradhan, Akshat Shah

Institution: Indian Institute of Technology Indore

Language: English

Topic: Hydrological Forecasting Using AI

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDGs: Climate action, Industry, innovation and infrastructure

Open Access status: diamond

Funder: Indian Institute of Technology Indore