Prediction of household food insecurity levels in Ethiopia

Application of deep learning models

Datos Bibliográficos

ID22429751
AutoresHabtamu Tilaye Wubetie (0000-0001-8417-4048, Bahir Dar University, autor de correspondencia), Temesgen Zewotir (0000-0003-1503-8055, University of KwaZulu-Natal), Aweke A Mitku (0000-0002-6129-9558, University of the Witwatersrand), Zelalem G Dessie (0000-0001-9056-6822, Bahir Dar University)
Año2026
Volumen18
Número4
Páginas867-883
Fecha de publicación2026-08-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaFood Security (JOURNAL)
Identificadores de la revistaISSN: 1876-4517 • E-ISSN: 1876-4525
EditorialSpringer Science and Business Media LLC (PUBLISHER)
DOI10.1007/s12571-025-01638-1
OpenAlexW7125649140
IdiomaEN
Referencias citadas46

Food insecurity is a frequently occurring country-wide problem caused by shocks with adverse outcomes on health and the economy. The objective is to predict data-driven levels of household food insecurity by considering the effect of space, time, and statistically significant factors. This study analyzed Ethiopian socio-economic household panel data for the years 2012, 2014, and 2016, comprising a sample of 3,835 households. Predictions were made using transductive deep learning algorithms to capture spatial and temporal dynamics, with 75% of the data allocated for training and 25% for testing. The results from training the temporal graph neural network model (T-GCN) with 4- and 2-layer architectures using 64 and 32 hidden units indicated that the 4-layer T-GCN with 64 hidden units predicted household food insecurity levels with greater accuracy. The prediction of the test data using the T-GCN model with 64 hidden units and 4 layers (T-GCN(64,4)) achieved an accuracy of 75.81%, outperforming baseline models in capturing spatial dependency with GCN (69.72%) and temporal correlation with RNN (54.66%). Additionally, the T-GCN(64,4) effectively predicted household food insecurity across most parts of the country, with an accuracy of 79.77% for 2014 and 72.43% for 2016. The higher stable prediction accuracy of the T-GCN in all prediction horizons demonstrated its superior performance and indicated the effectiveness of the data-driven approach in leveling severity and determining the related factors of household food insecurity. Therefore, efforts should be focused on urbanization, education, employment, non-agricultural business, water and sanitation, soil protection, and farming practices to reduce the severity of food insecurity by addressing dependency ratios, shocks, land ownership, and early marriage.

Agriculture · Deep learning · Food insecurity · Food security · Predictive modelling · Child Nutrition and Water Access · Food Security and Health in Diverse Populations · Food Waste Reduction and Sustainability