Environmental plague monitoring : Desert Locust prediction with artificial intelligence and stochastic model

Desert locusts are known as the world’s most destructive migratory pest. In the context of the European project EO4EU and European Space Agency (ESA) project IDEAS, a service has been developed, divided in two parts: a first part as early warning to monitor suitable ecosystem for locust to breed, a second part as impact assessment simulating the evolution of swarms.

The first part aims at predicting favorable breeding grounds for desert locusts seven days in advance by checking environmental conditions of the previous fifty days. Used environmental variables are Soil water content, Precipitation, and Temperature from ERA-5 land (Copernicus Climate). Additionally, NDVI (Normalized Difference Vegetation Index) from MODIS plays a role in the prediction. Locust information for model training was taken from a presence-only dataset provided by FAO’s Locust Watch. Actual most effective model is a customized version of Maxent. The latter is a statistical model widely used by researchers for species distribution modeling (SDM) as it is designed to work with presence-only datasets. Our model keeps Maxent's principles modifying the internal structure replacing the linear machine learning model with a Gated recurrent unit (GRU). This enables the model learning complex patterns and better understand the temporal evolution of features. Input data are time series where every time-step is a 5-day average of the above mentioned environmental variables, 50 days into 10 time steps. Data have been split into train and validation sets by using as training locust findings from 2000 to 2019 and as  validation findings from 2020 to 2021. Since no locust absence information is provided, only two evaluation techniques are used: recall, which reaches 76%, and positively predicted area which is at ~17%.

The second part aims at evaluating the geographic footprint that adult locusts will have within a two-week time frame. The focus is on forecasting migration patterns, as locusts are able to travel long distances in short periods and explore new areas unpredictably. The strength of this model lies in its stochastic structure since it simulates an environmental-biased random walk on a 2D lattice, generating batches of diverse potential scenarios. This approach incorporates complex driving-factors for migrations and considers all various paths that swarms may take. Another strength is the ability to account for environmental conditions throughout the entire lifespan of desert locusts, enabling the prediction of future movements while also considering past ones. The model takes as input temperature and wind data while all the parameters and assumptions about the locust biology are taken from the FAO “Desert Locust Guidelines: Biology and Behavior”. Collecting environmental variables is essential, as they not only trigger migration events but also determine the direction and speed of swarm movement. Finally, the model produces output maps that estimate the probabilities of future appearance of swarms and their potential sizes.

Predicted results for both parts are showing promising correlation with FAO reports on desert locust activity, additionally ground verification are on-going in order to test the performance of the model.