[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123002-en":3,"doc-seo-123002-105":30,"detail-sidebar-cat-0-en-105":83},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123002,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Machine Learning Classification-Regression Schemes for Desert Locust Presence Prediction in Western Africa","Desert locust outbreaks impose major socio-economic and agricultural impacts, yet forecasting occurrence is difficult because databases often contain limited sightings records. This paper introduces a methodology that combines classification and regression to predict both the presence of desert locusts and the expected number of individuals. Multiple machine learning techniques are evaluated, including linear regression, support vector machines, decision trees, random forests, and neural networks, using meteorological variables derived from ERA5 reanalysis for Western Africa scenarios.","applied sciences  \nArticle  \nMachine Learning Classification–Regression Schemes for Desert Locust Presence Prediction in Western Africa  \nL. Cornejo-Bueno 1, *, J. Pérez-Aracil 1, C. Casanova-Mateo 2, J. Sanz-Justo 3 and S. Salcedo-Sanz 1  \nCitation: Cornejo-Bueno, L.;  \nPérez-Aracil, J.; Casanova-Mateo, C.; Sanz-Justo, J.; Salcedo-Sanz, S. Machine Learning Classification– Regression Schemes for Desert Locust Presence Prediction in Western Africa. Appl. Sci. 2023, 13, 8266. [https://](https://)[ ](https://)[doi.org/10.3390/app13148266](doi.org/10.3390/app13148266)  \n1 Department of Signal Processing and Communications, Universidad de Alcalá,  \n28805 Alcalá de Henares, Spain; [jorge.perezaracil@uah.es](jorge.perezaracil@uah.es) (J.P.-A.); [sancho.salcedo@uah.es](sancho.salcedo@uah.es) (S.S.-S.)  \n2 Department of Information Systems, Universidad Politécnica de Madrid, 28031 Madrid, Spain; [carlos.casanova@upm.es](carlos.casanova@upm.es)  \n3 Laboratorio de Teledetección (LATUV), Remote Sensing Laboratory, Universidad de Valladolid,  \n47002 Valladolid, Spain; [julia.sanz.justo@uva.es](julia.sanz.justo@uva.es)  \n* Correspondence: [laura.cornejo@uah.es](laura.cornejo@uah.es)  \nAbstract: For decades, humans have been confronted with numerous pest species, with the desert locust being one of the most damaging and having the greatest socio-economic impact. Trying to predict the occurrence of such pests is often complicated by the small number of records and observations in databases. This paper proposes a methodology based on a combination of classification and regression techniques to address not only the problem of locust sightings prediction, but also the number of locust individuals that may be expected. For this purpose, we apply different machine learning (ML) and related techniques, such as linear regression, Support Vector Machines, decision trees, random forests and neural networks. The considered ML algorithms are evaluated in three different scenarios in Western Africa, mainly Mauritania, and for the elaboration of the forecasting process, a number of meteorological variables obtained from the ERA5 reanalysis data are used as input variables for the classification–regression machines. The results obtained show good performance in terms of classification (appearance or not of desert locust), and acceptable regression results in terms of predicting the number of locusts, a harder problem due to the small number of samples available. We observed that the RF algorithm exhibited exceptional performance in the classification task (presence/absence) and achieved noteworthy results in regression (number of sightings), being the most effective machine learning algorithm among those used. It achieved classification results, in terms of F-score, around the value of 0.9 for the proposed Scenario 1 .  \nKeywords: desert locusts; classification; regression; machine learning methods  \n1. Introduction  \nAcademic Editor: Zhengjun Qiu  \nReceived: 16 May 2023  \nRevised: 21 June 2023  \nAccepted: 5 July 2023  \nPublished: 17 July 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \nThe desert locust (Schistocerca gregaria sp.) is considered one of the most dangerous migratory pest species in the world [1,2], due to its fast reproduction time, capability of adaptation to and survival in difficult conditions [3], ability to migrate long distances and its harmful impact on crops [4] . Specifically, plagues of desert locusts have swept the Western Africa region for centuries [5], with high impacts in terms of crops losses and food scarcity, on the environment (due to pesticides and treatments against these insects [6]) and finally on the economy of the a","cbCaij2A903tauQq","https://ap.wps.com/l/cbCaij2A903tauQq","pdf",1806743,1,16,"English","en",105,"# Introduction\n## Proposed classification–regression methodology\n## Machine learning models and evaluation scenarios\n## Results and discussion","[{\"question\":\"What data are used as inputs for the forecasting models?\",\"answer\":\"Meteorological variables obtained from ERA5 reanalysis data are used as input features for the classification–regression machines.\"}]","Machine Learning Classification-Regression Schemes for Desert Locust Presence Prediction in Western Africa | PDF",1785814133,40,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":78,"head_meta":80,"extra_data":82,"updated_unix":28},"machine-learning-classification-regression-schemes-for-desert-locust-presence-prediction-in-western-africa","",{"@graph":36,"@context":77},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-classification-regression-schemes-for-desert-locust-presence-prediction-in-western-africa/123002/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71],{"name":72,"@type":73,"acceptedAnswer":74},"What data are used as inputs for the forecasting models?","Question",{"text":75,"@type":76},"Meteorological variables obtained from ERA5 reanalysis data are used as input features for the classification–regression machines.","Answer","https://schema.org",{"og:url":52,"og:type":79,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":81,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":84},[85,89,93,97,102,107,111,114,119,122,126],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":86,"show_sort_weight":87,"slug":88},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":90,"show_sort_weight":91,"slug":92},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Exam",70,"exam",{"id":98,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},5,"Comic",60,"comic",{"id":103,"doc_module":4,"doc_module_name":46,"category_name":104,"show_sort_weight":105,"slug":106},6,"Technology",50,"technology",{"id":108,"doc_module":4,"doc_module_name":46,"category_name":109,"show_sort_weight":29,"slug":110},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":112,"slug":113},30,"research-report",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":98,"slug":129},19,"General","general"]