Cloudbursts are sudden and highly localized extreme rainfall events that occur within a short duration and often lead to flash floods, landslides, and severe infrastructure damage. These events are particularly dangerous in mountainous and high-risk regions where warning time is limited. Existing cloudburst prediction systems mainly rely on satellite imagery, weather radars, and large-scale meteorological models. Although these approaches provide regional forecasts, they often fail to deliver accurate hyperlocal predictions due to the lack of real-time ground-level environmental data. This limitation results in delayed warnings and reduces the effectiveness of disaster preparedness. This paper proposes a decentralized Internet of Things and Machine Learning based framework for hyperlocal cloudburst prediction and early warning. The system deploys low-cost IoT sensor nodes in cloudburst-prone regions to continuously monitor environmental parameters such as rainfall intensity, soil moisture, temperature, and humidity. The real-time sensor data is combined with historical weather datasets to improve prediction accuracy and provide a better understanding of local atmospheric conditions. A Random Forest machine learning model is used to analyze the integrated dataset and predict cloudburst risk levels. The model learns patterns from historical data and classifies real- time weather conditions into safe or high-risk categories. The prediction performance is evaluated using metrics such as accuracy, precision, recall, and F1-score to ensure reliable results. When high-risk conditions are detected, the system generates real-time alerts through a mobile application to notify authorities and nearby communities. By integrating IoT-based sensing with machine learning prediction, the proposed system enhances early warning capability, improves disaster preparedness, and helps reduce potential loss of life and property caused by cloudburst events.
Dr. A. V. Santhosh Babu, Magesh Hariram K, Hariharan S, Sowmiya A, Nisha G (Thu,) studied this question.