A data-driven approach to mitigating food insecurity and achieving zero hunger: A case study of West African countries

This study employed a data-driven approach to analyze the long and short-run nexus between food insecurity, climate and economic shocks, population growth, urbanization, and population displacement for all West African countries by using annual time series data spanning 2000-2016. While the results show a unidirectional causal relationship running from climate and economic shocks, population growth, and urbanization to food insecurity, there exists no causal relationship between population displacement and food insecurity. In accordance with the Sustainable Development Goal 2 of achieving zero hunger for sustainable development, we formulate long short-term memory (LSTM) recurrent neural network (RNN) algorithm devoid of assumptions to forecast food insecurity for West African countries. Based on the result of our algorithm, we propose food insecurity mitigation pathways for the countries employed in this study. The food insecurity mitigation pathways reveal that strengthening current and future policies that mitigate food insecurity based on our projections is enough to mitigate the triggers of food insecurity in sustainable ways.