Year: 2026
Type: article
Abstract: 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.
Institution: Bapuji Institute of Engineering and Technology
Language: English
Topic: Arduino and IoT Applications
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7224812126