Abstract:

Climate change is a severe and growing challenge facing Nigeria, with significant impacts on the country's economy, environment, and society. To cope with this challenge, there is a need for climate change mitigation and adaptation strategies that are evidence-based and tailored to the country's unique vulnerabilities and opportunities. This study aimed to develop such strategies using data mining techniques. A range of climate and non-climate data sources were explored to identify key drivers of climate change in Nigeria and their associated impacts on different sectors, such as agriculture, water resources, and energy. Subsequently, data mining algorithms, including decision trees, clustering, and association rule mining, were applied to model and analyze the complex relationships between these drivers and their impacts on the sectors. The study found that a combination of mitigation and adaptation measures could be effective in reducing the severity of climate change impacts in Nigeria. These measures include promoting the use of renewable energy sources, improving water-use efficiency, and developing climate-smart agricultural practices. The data mining techniques used in this study proved useful for identifying these measures and for predicting their effectiveness under different scenarios. The results of this study provide important insights to policymakers and practitioners in Nigeria and other countries facing similar climate change challenges. It also highlights the potential of data mining techniques for developing climate change mitigation and adaptation strategies that are evidence-based, effective, and tailored to specific local contexts. Overall, this study contributes to the growing body of literature on leveraging data mining and machine learning techniques for addressing complex environmental and societal challenges.