AI-Driven Drought Management System: A Turkish Case Study

dc.authorscopusid 58876495700
dc.authorscopusid 58660205000
dc.contributor.author Sabamehr,M.
dc.contributor.author Ekin,C.C.
dc.contributor.other Computer Engineering
dc.date.accessioned 2024-07-05T15:50:26Z
dc.date.available 2024-07-05T15:50:26Z
dc.date.issued 2023
dc.department Atılım University en_US
dc.department-temp Sabamehr M., Atilim University, Department of Software Engineering, Ankara, Turkey; Ekin C.C., Atilim University, Department of Computer Engineering, Ankara, Turkey en_US
dc.description.abstract Nowadays, 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.citationcount 0
dc.identifier.doi 10.1109/IISEC59749.2023.10391008
dc.identifier.isbn 979-835031803-6
dc.identifier.scopus 2-s2.0-85184666363
dc.identifier.uri https://doi.org/10.1109/IISEC59749.2023.10391008
dc.identifier.uri https://hdl.handle.net/20.500.14411/4145
dc.institutionauthor Ekin, Cansu Çiğdem
dc.language.iso en en_US
dc.publisher Institute of Electrical and Electronics Engineers Inc. en_US
dc.relation.ispartof 4th 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 -- 196814 en_US
dc.relation.publicationcategory Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.scopus.citedbyCount 2
dc.subject Artificial intelligence en_US
dc.subject Drought management en_US
dc.subject Drought prediction en_US
dc.subject Machine learning en_US
dc.subject Standardized Precipitation Index (SPI) en_US
dc.subject Temperature average en_US
dc.subject Time series en_US
dc.title AI-Driven Drought Management System: A Turkish Case Study en_US
dc.type Conference Object en_US
dspace.entity.type Publication
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