Publicação
LocustLens: leveraging environmental data fusion and machine learning for desert locust swarm prediction
| Resumo: | The desert locust is one of the most destructive locusts recorded in human history, and it has caused significant food shortages, monetary losses, and environmental calamities. Prediction of locust attacks is complicated as it depends on various environmental and geographical factors. This research aims to develop a machine-learning model for predicting desert locust attacks in 42 countries that considers three predictors: soil moisture, maximum temperature, and precipitation. We developed the Global Locust Attack Database for 42 countries (GLAD42) by integrating TerraClimate’s environmental data with locust swarm attack data from the Food and Agriculture Organization (FAO). To improve the usability of spatial data, reverse geocoding which is the process of converting geographic coordinates (longitude and latitude) into humanreadable location names (such as countries and regions) was employed. This step enhances the clarity and interpretability of the data by providing meaningful geographic context. This study’s initial dataset focused on instances where locust attacks were recorded (positive class). To ensure a comprehensive analysis, we also incorporated negative class instances, representing periods (specific years and months) in the same countries and regions where locust attacks did not occur. This research utilizes the benefits of lazy learners by employing the K-nearest neighbor algorithm (K-NN), which provides high accuracy and the benefit of no time-consuming retraining even if real-time updated data is periodically added to the system. This research also focuses on building an eco-friendly machine learning model by evaluating carbon emissions from ML models. The results obtained from LocustLens are compared with other machine learning models, including baseline–K-NN, decision trees (DT), Logistic regression (LR), AdaBoost Classifier, BaggingClassifier, and support vector classifier (SVC). LocustLens outperformed all competitors with an accuracy of 98%, while baseline-K-NN achieved 96%, SVC gave 91%, DT gave 97%, AdaBoost has accuracy of 91%, BaggingClassifier gave 94% and LR gave 83%, respectively. Carbon emissions from RAM and CPU electricity consumption are measured in kg gCO2. They are a minimum for AdaBoost Classifier equal to 0.02 and 0.07 for DT and a maximum of 9.03 for SVC. The carbon footprint of LocustLens is 4.87 kg gCO2. |
|---|---|
| Autores principais: | Khan, Sidra |
| Outros Autores: | Akram, Beenish Ayesha; Zafar, Amna; Wasim, Muhammad; Khurshid, Khaldoon S.; Pires, Ivan Miguel |
| Assunto: | Environmental data Data fusion Artificial intelligence Desert locust Agriculture Data processing Machine learning |
| Ano: | 2024 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade de Aveiro |
| Idioma: | inglês |
| Origem: | RIA - Repositório Institucional da Universidade de Aveiro |
| _version_ | 1866173250274328576 |
|---|---|
| author | Khan, Sidra |
| author2 | Akram, Beenish Ayesha Zafar, Amna Wasim, Muhammad Khurshid, Khaldoon S. Pires, Ivan Miguel |
| author2_role | author author author author author |
| author_facet | Khan, Sidra Akram, Beenish Ayesha Zafar, Amna Wasim, Muhammad Khurshid, Khaldoon S. Pires, Ivan Miguel |
| author_role | author |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Khan, Sidra\"},{\"Person.name\":\"Akram, Beenish Ayesha\"},{\"Person.name\":\"Zafar, Amna\"},{\"Person.name\":\"Wasim, Muhammad\"},{\"Person.name\":\"Khurshid, Khaldoon S.\"},{\"Person.name\":\"Pires, Ivan Miguel\"}] |
| datacite.creators.creator.creatorName.fl_str_mv | Khan, Sidra Akram, Beenish Ayesha Zafar, Amna Wasim, Muhammad Khurshid, Khaldoon S. Pires, Ivan Miguel |
| datacite.date.Accepted.fl_str_mv | 2024-10-28T00:00:00Z |
| datacite.date.available.fl_str_mv | 2025-01-13T15:42:45Z |
| datacite.date.embargoed.fl_str_mv | 2025-01-13T15:42:45Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Environmental data Data fusion Artificial intelligence Desert locust Agriculture Data processing Machine learning |
| datacite.titles.title.fl_str_mv | LocustLens: leveraging environmental data fusion and machine learning for desert locust swarm prediction |
| dc.creator.none.fl_str_mv | Khan, Sidra Akram, Beenish Ayesha Zafar, Amna Wasim, Muhammad Khurshid, Khaldoon S. Pires, Ivan Miguel |
| dc.date.Accepted.fl_str_mv | 2024-10-28T00:00:00Z |
| dc.date.available.fl_str_mv | 2025-01-13T15:42:45Z |
| dc.date.embargoed.fl_str_mv | 2025-01-13T15:42:45Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10773/43348 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | PeerJ Inc. |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | Environmental data Data fusion Artificial intelligence Desert locust Agriculture Data processing Machine learning |
| dc.title.fl_str_mv | LocustLens: leveraging environmental data fusion and machine learning for desert locust swarm prediction |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_6501 |
| description | The desert locust is one of the most destructive locusts recorded in human history, and it has caused significant food shortages, monetary losses, and environmental calamities. Prediction of locust attacks is complicated as it depends on various environmental and geographical factors. This research aims to develop a machine-learning model for predicting desert locust attacks in 42 countries that considers three predictors: soil moisture, maximum temperature, and precipitation. We developed the Global Locust Attack Database for 42 countries (GLAD42) by integrating TerraClimate’s environmental data with locust swarm attack data from the Food and Agriculture Organization (FAO). To improve the usability of spatial data, reverse geocoding which is the process of converting geographic coordinates (longitude and latitude) into humanreadable location names (such as countries and regions) was employed. This step enhances the clarity and interpretability of the data by providing meaningful geographic context. This study’s initial dataset focused on instances where locust attacks were recorded (positive class). To ensure a comprehensive analysis, we also incorporated negative class instances, representing periods (specific years and months) in the same countries and regions where locust attacks did not occur. This research utilizes the benefits of lazy learners by employing the K-nearest neighbor algorithm (K-NN), which provides high accuracy and the benefit of no time-consuming retraining even if real-time updated data is periodically added to the system. This research also focuses on building an eco-friendly machine learning model by evaluating carbon emissions from ML models. The results obtained from LocustLens are compared with other machine learning models, including baseline–K-NN, decision trees (DT), Logistic regression (LR), AdaBoost Classifier, BaggingClassifier, and support vector classifier (SVC). LocustLens outperformed all competitors with an accuracy of 98%, while baseline-K-NN achieved 96%, SVC gave 91%, DT gave 97%, AdaBoost has accuracy of 91%, BaggingClassifier gave 94% and LR gave 83%, respectively. Carbon emissions from RAM and CPU electricity consumption are measured in kg gCO2. They are a minimum for AdaBoost Classifier equal to 0.02 and 0.07 for DT and a maximum of 9.03 for SVC. The carbon footprint of LocustLens is 4.87 kg gCO2. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | article |
| id | ria_4bfb77aae0b5a027a8564a7a09b23738 |
| identifier.url.fl_str_mv | http://hdl.handle.net/10773/43348 |
| instacron_str | ua |
| institution | Universidade de Aveiro |
| instname_str | Universidade de Aveiro |
| language | eng |
| network_acronym_str | ria |
| network_name_str | RIA - Repositório Institucional da Universidade de Aveiro |
| oai_identifier_str | oai:ria.ua.pt:10773/43348 |
| organization_str_mv | urn:organizationAcronym:ua |
| person_str_mv | Khan, Sidra Akram, Beenish Ayesha Zafar, Amna Wasim, Muhammad Khurshid, Khaldoon S. Pires, Ivan Miguel |
| publishDate | 2024 |
| publisher.none.fl_str_mv | PeerJ Inc. |
| reponame_str | RIA - Repositório Institucional da Universidade de Aveiro |
| repository_id_str | urn:repositoryAcronym:ria |
| service_str_mv | urn:repositoryAcronym:ria |
| spelling | pt_PTThe desert locust is one of the most destructive locusts recorded in human history, and it has caused significant food shortages, monetary losses, and environmental calamities. Prediction of locust attacks is complicated as it depends on various environmental and geographical factors. This research aims to develop a machine-learning model for predicting desert locust attacks in 42 countries that considers three predictors: soil moisture, maximum temperature, and precipitation. We developed the Global Locust Attack Database for 42 countries (GLAD42) by integrating TerraClimate’s environmental data with locust swarm attack data from the Food and Agriculture Organization (FAO). To improve the usability of spatial data, reverse geocoding which is the process of converting geographic coordinates (longitude and latitude) into humanreadable location names (such as countries and regions) was employed. This step enhances the clarity and interpretability of the data by providing meaningful geographic context. This study’s initial dataset focused on instances where locust attacks were recorded (positive class). To ensure a comprehensive analysis, we also incorporated negative class instances, representing periods (specific years and months) in the same countries and regions where locust attacks did not occur. This research utilizes the benefits of lazy learners by employing the K-nearest neighbor algorithm (K-NN), which provides high accuracy and the benefit of no time-consuming retraining even if real-time updated data is periodically added to the system. This research also focuses on building an eco-friendly machine learning model by evaluating carbon emissions from ML models. The results obtained from LocustLens are compared with other machine learning models, including baseline–K-NN, decision trees (DT), Logistic regression (LR), AdaBoost Classifier, BaggingClassifier, and support vector classifier (SVC). LocustLens outperformed all competitors with an accuracy of 98%, while baseline-K-NN achieved 96%, SVC gave 91%, DT gave 97%, AdaBoost has accuracy of 91%, BaggingClassifier gave 94% and LR gave 83%, respectively. Carbon emissions from RAM and CPU electricity consumption are measured in kg gCO2. They are a minimum for AdaBoost Classifier equal to 0.02 and 0.07 for DT and a maximum of 9.03 for SVC. The carbon footprint of LocustLens is 4.87 kg gCO2.application/pdfengPeerJ Inc.pt_PTLocustLens: leveraging environmental data fusion and machine learning for desert locust swarm predictionKhan, SidraAkram, Beenish AyeshaZafar, AmnaWasim, MuhammadKhurshid, Khaldoon S.Pires, Ivan MiguelHandlehttp://hdl.handle.net/10773/43348ISSNIsPartOf2376-5992DOIIsPartOf10.7717/peerj-cs.24202025-01-13T15:42:45Z2024-10-28T00:00:00Z2024-10-28http://purl.org/coar/access_right/c_abf2open accesspt_PTEnvironmental datapt_PTData fusionpt_PTArtificial intelligencept_PTDesert locustpt_PTAgriculturept_PTData processingpt_PTMachine learning4431825 byteshttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://ria.ua.pt/bitstream/10773/43348/1/peerj-cs-2420.pdfliteraturehttp://purl.org/coar/resource_type/c_6501journal article |
| spellingShingle | LocustLens: leveraging environmental data fusion and machine learning for desert locust swarm prediction Khan, Sidra Environmental data Data fusion Artificial intelligence Desert locust Agriculture Data processing Machine learning |
| status | SINGLETON |
| subject.fl_str_mv | Environmental data Data fusion Artificial intelligence Desert locust Agriculture Data processing Machine learning |
| title | LocustLens: leveraging environmental data fusion and machine learning for desert locust swarm prediction |
| title_full | LocustLens: leveraging environmental data fusion and machine learning for desert locust swarm prediction |
| title_fullStr | LocustLens: leveraging environmental data fusion and machine learning for desert locust swarm prediction |
| title_full_unstemmed | LocustLens: leveraging environmental data fusion and machine learning for desert locust swarm prediction |
| title_short | LocustLens: leveraging environmental data fusion and machine learning for desert locust swarm prediction |
| title_sort | LocustLens: leveraging environmental data fusion and machine learning for desert locust swarm prediction |
| topic | Environmental data Data fusion Artificial intelligence Desert locust Agriculture Data processing Machine learning |
| topic_facet | Environmental data Data fusion Artificial intelligence Desert locust Agriculture Data processing Machine learning |
| url | http://hdl.handle.net/10773/43348 |
| visible | 1 |