Published April 2, 2026 | Version v1
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A Decentralized IoT and Machine Learning Framework for Hyperlocal Cloudburst Prediction

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Description

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.

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Dates

Submitted
2026-04-02
Cloudbursts are sudden and intense rainfall events that occur within a very short duration and over a limited geographical area, often leading to severe consequences such as flash floods, landslides, and destruction of infrastructure. These events are particularly dangerous in mountainous and disaster-prone regions where the response time for emergency measures is minimal. Traditional weather forecasting systems primarily depend on satellite observations, Doppler weather radars, and large-scale meteorological models. Although these approaches are effective for regional forecasting, they often fail to provide accurate predictions at a hyperlocal level due to the absence of real-time ground-level environmental data .

References

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