AI-Driven Drought Management System: A Turkish Case Study

dc.authorscopusid58876495700
dc.authorscopusid58660205000
dc.contributor.authorSabamehr,M.
dc.contributor.authorEkin,C.C.
dc.contributor.otherComputer Engineering
dc.date.accessioned2024-07-05T15:50:26Z
dc.date.available2024-07-05T15:50:26Z
dc.date.issued2023
dc.departmentAtılım Universityen_US
dc.department-tempSabamehr M., Atilim University, Department of Software Engineering, Ankara, Turkey; Ekin C.C., Atilim University, Department of Computer Engineering, Ankara, Turkeyen_US
dc.description.abstractNowadays, drought is one of the trending topics in the world that has turned into a challenge for the world. By developing countries and cities worldwide, especially in the economic aspect, governments started to damage the environment such as through the use of fossil fuels, pollution of the seas, unregulated use of fresh water also deforestation for personal purposes. The presented research aims to change the format of drought mitigation strategies from traditional ways into the up to date treats. Leveraging AI technologies, including machine learning algorithms and data analytics, a comprehensive AI-driven drought management system is designed and implemented. In this system, inconsistent data have been obtained from the Ministry of Agriculture and Forestry organization and transformed into insightful data and analyzed in real-Time style to provide the status of agricultural products in Turkey. This research contributes to the fields of environmental science and agriculture by innovatively augmenting traditional approaches with AI-driven solutions. Ultimately, our research offers a means to monitor weather conditions in different regions of Turkey, moving beyond manual drought prediction and guesswork that were prevalent in previous systems. Additionally, it facilitates the evaluation of vegetation health by considering precipitation and temperature averages in each area. © 2023 IEEE.en_US
dc.identifier.citation0
dc.identifier.doi10.1109/IISEC59749.2023.10391008
dc.identifier.isbn979-835031803-6
dc.identifier.scopus2-s2.0-85184666363
dc.identifier.urihttps://doi.org/10.1109/IISEC59749.2023.10391008
dc.identifier.urihttps://hdl.handle.net/20.500.14411/4145
dc.institutionauthorEkin, Cansu Çiğdem
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof4th International Informatics and Software Engineering Conference - Symposium Program, IISEC 2023 -- 4th International Informatics and Software Engineering Conference, IISEC 2023 -- 21 December 2023 through 22 December 2023 -- Ankara -- 196814en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial intelligenceen_US
dc.subjectDrought managementen_US
dc.subjectDrought predictionen_US
dc.subjectMachine learningen_US
dc.subjectStandardized Precipitation Index (SPI)en_US
dc.subjectTemperature averageen_US
dc.subjectTime seriesen_US
dc.titleAI-Driven Drought Management System: A Turkish Case Studyen_US
dc.typeConference Objecten_US
dspace.entity.typePublication
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