Researchers build ML models to forecast food insecurity • The Register

An international team of researchers have built a set of machine learning models they say can help predict global food shortages in the near future, helping governments and international agencies understand where they can best help.
Scientists from the World Food Programme, University of London Mathematics Department and Central European University Department of Network and Data Science, made use of a “unique global dataset” to build machine learning models that can explain up to 81 percent of the variation in insufficient food consumption.
The study claims the machine learning models draw from indirect data sources in areas such as food prices, macro-economic indicators (including GDP), weather, conflict, prevalence of undernourishment, population density, and previous food insecurity trends. The aim is to create near-term forecasts, or “nowcasts.”

“We show that the proposed models can nowcast the food security situation in near real-time and propose a method to identify which variables are driving the changes observed in predicted trends — which is key to make predictions serviceable to decision-makers,” the research paper published in Nature Food this week said.

The outputs of the ML models have been used to create a world map including near-term food insecurity forecasts called HungerMap.

In 2019, the number of undernourished people was estimated to be 650 million, with 135 million in 55 countries and territories reported to be acutely “food-insecure.” Food insecure is defined as lacking consistent access to enough food so you can lead an active, healthy life. Following the global COVID-19 pandemic, these numbers shot up. At least 280 million people were reported to be acutely food insecure in 2020, more than doubling the number from the previous year.
Governments and international organizations such as the World Food Programme (WFP), the Food and Agriculture Organization (FAO) and the World Bank measure food security with face-to-face surveys or remote mobile phone surveys. But these can be costly, while accuracy can be a problem. “Food insecurity is a more dynamic and unstable phenomenon than poverty, with a seasonal component related to agricultural production calendars and subject to swift changes when external shocks hit, therefore requiring more frequent and rapid assessments,” the paper said.
“This opens the door to food security near-real time nowcasting on a global scale, allowing decision-makers to make more timely and informed decisions on policies and programmes oriented towards the fight against hunger,” the authors said.

The researchers also made use of secondary data to predict food insecurity in the longer term. Agricultural production, statistical crop models and climate modelling have been used to make projections to 2030 about crop production changes. Meanwhile, anonymized and aggregated mobile phone data has been used in Senegal to look at the broader movement of people across the seasons, and was combined with agricultural calendars and records of precipitation to characterize food security.
The current study did not use mobile phone data because it is typically obtained through national mobile phone operators. “Therefore, it is not a type of data that is easily scalable, and this is why it was not a suitable data source for our global approach,” lead author Elisa Omodei, assistant professor at the Central European University told The Register.
The authors suggest when their models predict increases in the prevalence of food-insecure people, then the World Food Programme would trigger rapid assessments through face-to-face or remote surveys and mobilize in-country analysts to gain a better understanding of the situation.

“The development of these models is motivated by a specific need of WFP to fill a gap that currently exists because of limited resources and inaccessibility, that is, to provide regular information for less reachable places, where food security assessments are carried out only once or twice per year but that nevertheless require a constant flow of information to inform humanitarian operations,” the paper said. ®