Master of Science in Data Science and Analytics

Permanent URI for this collectionhttps://hdl.handle.net/20.500.11951/1204

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Browsing Master of Science in Data Science and Analytics by Author "Arinaitwe, Philip"

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    Enhancing food security forecasting in Uganda: a geospatial Analytics framework with crop yield integration
    (Uganda Christian University, 2026-09-30) Arinaitwe, Philip
    Food security is a crucial pillar for human well being, societal stability and national development. Food security can be defined as a situation when all people, at all times, have physical and economic access to sufficient, safe and nutritious food that meets their dietary needs and food preferences for an active and healthy life. In Uganda, nearly 20 percent of the population lives below the poverty line and over 16 million Ugandans face food insufficiency due to various factors like unreliable climate, economic instability, population pressure, conflict and inadequate forecasting mechanisms. Recent research studies have increasingly turned to predictive modelling using machine learning for food security forecasting, integration of climate data, socio-economic indicators and innovative geospatial analytics. Whereas advances in machine learning and geospatial analytics have led to significant improvements in climatic modelling for food security, they often fall short by excluding actual agricultural outputs. Without integrating crop yield data, forecasts may misestimate food availability yet it can be a key driver of food crises. Particularly for Uganda, where over 70% of the population is employed by agriculture, the omission of crop yield data from forecasting models can result in inaccurate predictions/forecasts. To address this gap, this study developed and evaluated a geospatial machine learning framework for forecasting food security in Uganda by integrating crop yield data from FAOSTAT with multi-source environmental, market, conflict, demographic and historical Integrated Food Security Phase Classification (IPC) data from 2007 to 2020. A hybrid model combining a Long Short-Term Memory (LSTM) temporal encoder and an XGBoost classifier was trained and validated on 44, 082 district-month observations. The hybrid LSTM–XGBoost model achieved a macro F1-score of 0.75 and an overall accuracy of 93.8% on the test set. Importantly, the model demonstrated exceptional performance in identifying severe food crises (IPC 3+), achieving a recall of 0.82, a ROC-AUC of 0.998 and a PR-AUC of 0.852. An ablation study confirmed that incorporating crop yield data significantly improved predictive power, increasing the IPC 3+ F1-score from 0.688 to 0.759. Furthermore, model comparisons demonstrated that a country-specific model trained purely on Ugandan observations significantly outperformed a consolidated regional model shown by a macro F1 score of 0.747 achieved by the country-specific model as compared to the consolidated regional model with a macro F1-score of 0.725. These results provide robust empirical evidence that integrating crop yield data into geospatial frameworks enhances food security early warning systems which enables timely, targeted interventions to mitigate severe hunger.