Drought Modelling Using Artificial Intelligence Algorithms in Izmir District
| dc.contributor.author | Zeynep İrem Özen | |
| dc.contributor.author | Berk Sadettin Tengerlek | |
| dc.contributor.author | Damla Yüksel | |
| dc.contributor.author | Efthimia Staiou | |
| dc.contributor.author | Mir Jafar Sadegh Safari | |
| dc.contributor.editor | N.M. Durakbasa , M.G. Gençyılmaz | |
| dc.date.accessioned | 2025-10-06T17:50:14Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | The world's water resources are decreasing day by day due to factors such as climate change drought inefficient pricing policies implemented by the government population growth uncontrolled water consumption technological developments and industrialization. A decrease in water resources causes water scarcity in the long-term period. This study is conducted to analysis the meteorological drought in Izmir district Turkey. Inspired by the real-life problem drought estimation models are developed through artificial neural network-based artificial intelligence techniques incorporating a decision support system. The Z-score index (ZSI) values are computed using precipitation data collected from five meteorological station in Küçük Menderes basin and several developed models are compared according to the variety of statistical performance metrics. © 2022 Elsevier B.V. All rights reserved. | |
| dc.identifier.doi | 10.1007/978-3-030-90421-0_59 | |
| dc.identifier.isbn | 9789819650583, 9783031991585, 9783031948886, 9789819667314, 9789811937156, 9783030703318, 9789811622779, 9789811969447, 9789819701056, 9789819748051 | |
| dc.identifier.issn | 21954364, 21954356 | |
| dc.identifier.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119865946&doi=10.1007%2F978-3-030-90421-0_59&partnerID=40&md5=114f127266e9cfb21c25577174b724c6 | |
| dc.identifier.uri | https://gcris.yasar.edu.tr/handle/123456789/8853 | |
| dc.language.iso | English | |
| dc.publisher | Springer Science and Business Media Deutschland GmbH | |
| dc.relation.ispartof | International Symposium for Production Research ISPR2021 | |
| dc.source | Lecture Notes in Mechanical Engineering | |
| dc.subject | Artificial Neural Networks, Drought, Feed Forward Backpropagation, Generalized Regression, Radial Basis Function, Z-score Index, Climate Change, Decision Support Systems, Population Statistics, Radial Basis Function Networks, Water Resources, Artificial Intelligence Algorithms, Base Function, Drought Modeling, Feedforward Backpropagation, Generalized Regression, Radial Base Function, Radial Basis, Waters Resources, Z-score Index, Z-scores, Drought | |
| dc.subject | Climate change, Decision support systems, Population statistics, Radial basis function networks, Water resources, Artificial intelligence algorithms, Base function, Drought modeling, Feedforward backpropagation, Generalized regression, Radial base function, Radial basis, Waters resources, Z-score index, Z-scores, Drought | |
| dc.title | Drought Modelling Using Artificial Intelligence Algorithms in Izmir District | |
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| gdc.virtual.author | Safari, Mir Jafar Sadegh | |
| oaire.citation.endPage | 701 | |
| oaire.citation.startPage | 689 | |
| person.identifier.scopus-author-id | Özen- Zeynep İrem (57352151200), Tengerlek- Berk Sadettin (57352555900), Yüksel- Damla (57212210455), Staiou- Efthimia (57212215492), Safari- Mir Jafar Sadegh (56047228600) | |
| project.funder.name | This publication is supported as part of Project No. BAP095 entitled “Drought Assessment in Izmir District Turkey” has been approved by Yasar University Project Evaluation Commission (PEC). Special thanks to the Turkish Meteorology General Directorate (MGM) for providing the database used in this study. We would like to thank Dr. Babak Vaheddoost for his support and contributions throughout the project. Also we would like to thank our colleagues Ceyda Sepetçi Ülkü Simay Mandil Ata Çakar Ege Paker and Emre Karaoğlan for their help and contribution to the study. | |
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