  Locust infestation of some regions in the world, including Africa, Asia and
Middle East has become a concerning issue that can affect the health and the
lives of millions of people. In this respect, there have been attempts to
resolve or reduce the severity of this problem via detection and monitoring of
locust breeding areas using satellites and sensors, or the use of chemicals to
prevent the formation of swarms. However, such methods have not been able to
suppress the emergence and the collective behaviour of locusts. The ability to
predict the location of the locust swarms prior to their formation, on the
other hand, can help people get prepared and tackle the infestation issue more
effectively. Here, we use machine learning to predict the location of locust
swarms using the available data published by the Food and Agriculture
Organization of the United Nations. The data includes the location of the
observed swarms as well as environmental information, including soil moisture
and the density of vegetation. The obtained results show that our proposed
model can successfully, and with reasonable precision, predict the location of
locust swarms, as well as their likely level of damage using a notion of
density.
