Predicting Regional Locust Swarm Distribution with Recurrent Neural Networks
This work provides a tool for regional authorities and affected populations to prepare for and mitigate the impact of locust infestations, which is a significant problem for food security and livelihoods.
This paper addresses the challenge of predicting locust swarm locations by employing recurrent neural networks. The model successfully predicts the location of locust swarms and their likely damage level using FAO data on observed swarms, soil moisture, and vegetation density.
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.