Abdul Kareem, Razia Sulthana ORCID: https://orcid.org/0000-0001-5331-1310 (2026) Predictive Machine Learning Models for assessing the effects of land use and climate change on food affordability in the UK. In: ICICA '25: Proceedings of the 14th International Conference on Information Communication and Applications. Association for Computing Machinery (ACM), New York, pp. 49-56. ISBN 979-8400721151 (doi:10.1145/3743158.3783856)

Preview

PDF (Open Access Conference Paper) 52458 KAREEM ABDUL_Predictive_Machine_Learning_Models_For_Assessing_The_Effects_Of_Land_Use_(OA)_2026.pdf - Published Version Available under License Creative Commons Attribution. Download (1MB) | Preview

Abstract

This research analyses the complex trilateral relationship, land use pattern, climate change and consumer affordability of food products in the UK based on the data set collected from Food Agriculture and Organisation (FAO) from 1961 to 2022. Though agriculture contributes minimally to the UK’s GDP, it plays a very major role in economic stability and in building resilient and sustainable planet. Artificial intelligence is a critical tool that helps in understanding, forecasting and predicting patterns on the complex multidimensional data. This paper aims to apply AI techniques on the data to understand the patterns and dependencies. Initially, the data extracted from the FAO is analysed to understand the trends and the relationship between the attributes is identified using correlation matrix. Several hypotheses are framed, and classification and prediction machine learning algorithms are applied on them. Trend analysis reveals that a decrease in carbon dioxide emission is caused by expansion in the forest land with a very steady high increase in the cost of buying a healthy diet in the UK. Several machine learning models are applied on land use and climate emissions and the support vector regressor shows the highest performance with an R-squared value of 0.96. Furthermore, classification models are applied to get relation between the high and low forest growth regions where the decision tree and the random forest achieved the highest accuracy of 0.8. This research provides valuable insight into the fact that increasing the agriculture land does not reduce the affordability to buy healthy food. Hence, to economically stabilize, the UK should come up with different policies and measures to provide affordable healthy food to people and not just by increasing the agriculture land it can be achieved.

Item Type:	Conference Proceedings
Title of Proceedings:	ICICA '25: Proceedings of the 14th International Conference on Information Communication and Applications
Additional Information:	Citation in BibTeX format: ICICA 2025: The 14th International Conference on Information Communication and Applications September 14 - 16, 2025 Oxford, United Kingdom.
Uncontrolled Keywords:	agriculture, climate change, CO2 emissions, consumer affordability, United Kingdom (UK), Machine Learning, Sustainable Development Goals (SDGs)
Subjects:	Q Science > Q Science (General) Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS)
Last Modified:	12 Feb 2026 11:52
URI:	https://gala.gre.ac.uk/id/eprint/52458

Actions (login required)

View Item

Downloads