A Decentralized IoT and Machine Learning Framework for Hyperlocal Cloudburst Prediction

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.