Harnessing machine learning to address background radiation risks in Gwagwalada and Kwali Area Council

Gwagwalada and Kwali Area Council, bustling localities within the Federal Capital Territory of Nigeria, are home to thriving populations, extensive landfills, and dynamic residential activities. Yet, beneath this vibrant surface lies an invisible but significant environmental concern—background radiation. My recently concluded research, “Simulation Prediction of Background Radiations Using Machine Learning,” has focused on addressing this challenge using cutting-edge artificial intelligence (AI) techniques.

Understanding background radiation

Background radiation, originating from natural sources such as radon and terrestrial materials, as well as human-made activities, can have harmful effects on health and the environment. Many residents are unaware of these risks, leading to prolonged exposure. The need for scientific intervention to identify, predict, and mitigate these effects cannot be overstated.

Research insights and methodology

This research leveraged field data collected from 2,094 locations within Gwagwalada and Kwali Area Council. Using a Gamma Scout device, I measured alpha, beta, and gamma radiation levels and trained machine learning algorithms—including Random Forest and Support Vector Machines—to classify the radiation’s effects as harmful or harmless. The Random Forest model achieved a remarkable accuracy of 94%.

The study revealed areas of elevated radiation levels, particularly near landfills and sewage sites, providing critical insights into environmental risk zones. These findings underscore the potential of machine learning to revolutionize radiation monitoring and policy-making.

Implications for Gwagwalada and Kwali Area Council

The impact of this research extends beyond academia. By enabling precise radiation detection and prediction, it offers the local government, environmental agencies, and residents tools to address environmental hazards. Recommendations for radiation safety measures can now be tailored to specific neighborhoods, improving public health outcomes.

Call to action

As Gwagwalada and Kwali Area Council continue to grow, integrating AI-driven solutions into environmental management becomes imperative. This research provides a blueprint for such integration, demonstrating that technology can empower communities to safeguard their environment and health.

For the residents of Gwagwalada, Kwali Area Council, and policymakers alike, the message is clear: science and technology hold the key to a healthier, safer future.

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