Date of Award
BSc (Hons) in Computing in IT
The impact of climate change on agriculture is a growing concern worldwide, and Ireland is no exception. The purpose of this project is to use machine learning techniques to predict the effects of climate change on Irish agriculture and identify strategies for adaptation and mitigation. The project uses the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology to guide the data analysis process, MoSCoW prioritization to identify the most critical needs, and SWOT analysis to evaluate the strengths, weaknesses, opportunities, and threats our project may encounter. Historical temperature data for Ireland and Dublin will be used as our data sources. The project will use machine learning algorithms to predict the potential effects of climate change on agriculture and make recommendations for policymakers, farmers, and researchers to mitigate the potential effects of climate change on Irish agriculture. Using CRISP-DM as our framework, the project began with a thorough business understanding phase, where we identified the key stakeholders and their information needs, as well as the challenges and opportunities associated with climate change and agriculture in Ireland (FAO, 2016). This helped us to define our project objectives more clearly and to develop a comprehensive plan for data collection, analysis, and modelling.
Matsumoto, Rodrigo and Kuprian Carrinho, Sarah, "Predicting the effects of Climate Change on Irish Agriculture" (2023). ICT. 39.