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.

中文翻译：

---

基于递归神经网络的区域蝗虫种群分布预测

世界上某些地区（包括非洲，亚洲和中东）的蝗虫感染已成为一个令人担忧的问题，可能会影响数百万人的健康和生活。在这方面，已经尝试通过使用卫星和传感器检测和监测蝗虫繁殖区，或使用化学药品来防止虫群的形成来解决或减轻该问题的严重性。但是，这种方法不能抑制蝗虫的出现和集体行为。另一方面，能够预测蝗虫群形成之前的位置的能力可以帮助人们做好准备并更有效地解决虫害问题。这里，我们使用机器学习来利用联合国粮食及农业组织发布的可用数据来预测蝗虫群的位置。数据包括观察到的群的位置以及环境信息，包括土壤湿度和植被密度。获得的结果表明，我们提出的模型可以成功地且以合理的精度使用密度概念来预测蝗虫群的位置以及它们可能造成的破坏程度。