## 4 Conclusions  
In this paper, we have presented a novel way of dealing with the problem of desert locust prediction by means of a machine learning classification-regression approach. The idea is to not only deal with the presence/absence problem, but to also try to estimate the number of monthly sightings in three different scenarios for Western Africa. Three well-known classification algorithms (SVM, DT and RF) and five regression approaches have been considered (SVR, DT, RF, MLP and LR). An important number of predictive variables from reanalysis data and NDVI data are considered. We have found that the RF algorithm obtained the best results, both in classification (presence/absence) and regression (number of sightings), with excellent results in the classification task, and good results in regression, a harder problem to estimate. Some differences are spotted depending on the scenario considered, i.e., different regions of Western Africa, with different numbers of sightings in the training and test sets. In Scenario 1, the classification-regression ML approaches obtained the best results, and the worst prediction was obtained in Scenario 3 due to the smaller number of samples in this scenario.  
The results obtained in this work have shown that it is possible to extend the problem of desert locust prediction to evaluating the number of monthly sightings, and not only the presence/absence as considered in previous works. In general, the performance of the ML algorithms is good in this regression problem, though in general the performance in the classification problem is better, since this is an easier approach. Future research must deal with improvements in the performance of the regression algorithms, the application of new balancing methods for the classification tasks and the analysis of the performance when a small number of samples is available, which would allow the study to be extended to other areas with fewer numbers of sightings. Also, the testing of deep learning approaches such as LSTM networks or convolutional-based approaches over similar data is a real possibility which may improve the prediction of desert locust breeding area detection in the next few years.  
Conceptualization, J.P.-A., S.S.-S. and L.C.-B.; methodology, J.P.-A., C.C.-M. and S.S.-S.; software, L.C.-B. and J.P.-A.; validation, J.P.-A. and S.S.-S.; investigation, J.P.-A., S.S.-S., C.C.-M. and [PERSON]-J.; resources, S.S.-S. and J.P.-A.; data curation, L.C.-B., C.C.-M. and J.S.-J.; writing--original draft preparation, S.S.-S., J.P.-A. and L.C.-B.; writing--review and editing, S.S.-S., J.P.-A. and L.C.-B.; supervision, J.P.-A. and S.S.-S.; project administration, S.S.-S. and J.P.-A.; funding acquisition, S.S.-S. All authors have read and agreed to the published version of the manuscript.  
This research has been partially supported by the project PID2020-115454 GB-C21595 of the Spanish Ministry of Science and Innovation (MICINN).  
Not applicable.  
Figure 3: Performance of the best regression technique (RF) considered in the prediction problem, for Scenarios 1, 2 and 3: (**a**) temporal RF in Scenario 1; (**b**) scatter RF in Scenario 1; (**c**) temporal RF in Scenario 2; (**d**) scatter RF in Scenario 2; (**e**) temporal RF in Scenario 3; (**f**) scatter RF in Scenario 3.