## 2 Methodology  
In this paper, we consider an estimation problem to determine the presence or non-presence of locusts, as well as their number, in different scenarios. For this purpose, we have used time series of sightings as the target variable of the problem. These time series cover the years 2000 to 2015, and the geographical area of the study is West Africa, with Mauritania being the most interesting due to the large number of sightings. Presence data were obtained by downloading from the Locust Hub Initiative provided by the FAO ([[https://locust-hub-hqfao.hub.arcgis.com/](https://locust-hub-hqfao.hub.arcgis.com/)]([https://locust-hub-hqfao.hub.arcgis.com/](https://locust-hub-hqfao.hub.arcgis.com/))). From this data source, we were able to download the presence or absence of different types of locusts (hoppers, swarms and adults). Since in the time series of locusts there is no regular temporal or spatial continuity, it was decided to segment the problem as follows: In Figure 1, we can see the presence of locusts in green color. The geographical area under study has been divided into 3 regions, about 300 km in diameter, thus forming the three classification-regression scenarios addressed in the paper. In each of these scenarios, the forecasting process consists of monthly estimation of the presence and number of locusts. The exact number of sightings on the time series used, for each scenario, is as follows: Scenario 1 = 5414 (there are 5414 sightings in 123 days); Scenario 2 = 47,131 sightings in 210 days) and Scenario 3 = 8897 (there are 8897 sightings in 103 days). To obtain a good estimation, the forecast variables used were obtained from ERA5, the fifth-generation European Centre for Medium-Range Weather Forecasting (ECMWF) reanalysis constructed from observations and atmospheric models. These reanalysis data cover the period from 1959 to present, and their update frequency is every hour [29]. As mentioned above, the estimation is monthly and from 2000 to 2015, so the time series will be of length 192 (sixteen years times twelve months), so the reanalysis variables will be averaged to monthly data. In each study region, for Scenarios 1, 2 and 3, we downloaded the reanalysis meteorological variables at 5 geographical points, and for each point we obtained a total of 7 variables, listed below: surface pressure, total column water, total column water vapour, 10 m U wind component, 10 m V wind component, 2 m temperature and skin temperature. This makes a total of 35 exogenous reanalysis variables in each area for the regression-classification process. In addition, in each zone we have an extra variable, the Normalized Difference Vegetation Index (NDVI), because of the high correlation it has with the presence or absence of locusts. The selection of these variables for the process of predicting the presence of the Mauritanian locus is based on their relevance and potential relationship with the behavior and distribution of the locust. For example,  
* Surface pressure can influence weather patterns and the formation of high- and low-pressure systems that may affect the presence and movement of the locust. * Total column water and total column water vapor are related to the availability of moisture in the atmosphere, which can be an important factor for the development and migration of the locust. * The 10 m U and V wind components are relevant for understanding wind patterns and their influence on the dispersion and movement of the locust. * The 2 m temperature and skin temperature can affect the activity and behavior of the locust as its metabolism and movements may be related to thermal conditions. * NDVI provides information about vegetation and can be associated with food availability for the locust. Overall, these variables have been selected because they are considered to capture relevant climatic, environmental and ecological aspects related to the behavior and distribution of the Mauritanian locust.