AI Based Radiation Emission Risk, Environmental Impact, and Mitigation Planning System

DOI

https://www.doi.org/10.59256/ijsreat.20260605027

Published In

Volume 6, Issue 5 (2026)

Published Online

September-October 2026

Pages

226-230

Article Type

Original Article

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Radiation estimation plays an important role in understanding radiation levels and supporting effective environmental monitoring and risk assessment. This project presents a Radiation Estimation System using Machine Learning and the Internet of Things (IoT), integrating Random Forest Regression with real-time sensor data collection and web-based monitoring. An ESP32 microcontroller collects environmental parameters and transmits the readings to the ThingSpeak cloud platform. A machine learning model processes relevant environmental parameters to estimate radiation levels, while a Flask-based web application provides prediction results, historical records, and visual analysis. SQLite is used to store predictio n history for further reference. By combining environmental data collection, machine learning-based estimation, and accessible web monitoring, the proposed system aims to support radiation assessment, environmental safety, and informed decision-making. The project contributes to Sustainable Development Goal 3 (SDG 3): Good Health and Well-being, by promoting data-driven radiation monitoring and supporting awareness of potential radiation risks to human health and the environment.