Drought Modelling Using Artificial Intelligence Algorithms in Izmir District
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Date
2022
Authors
Zeynep Irem Ozen
Berk Sadettin Tengerlek
Damla Yuksel
Efthymia Staiou
Mir Jafar Sadegh Safari
Journal Title
Journal ISSN
Volume Title
Publisher
SPRINGER-VERLAG SINGAPORE PTE LTD
Open Access Color
Green Open Access
No
OpenAIRE Downloads
OpenAIRE Views
Publicly Funded
No
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 Kucuk Menderes basin and several developed models are compared according to the variety of statistical performance metrics.
Description
Keywords
Drought, Artificial neural networks, Feed forward backpropagation, Generalized regression, Radial basis function, Z-Score Index, NEURAL-NETWORKS, Feed Forward Backpropagation, Artificial Neural Networks, Generalized Regression, Radial Basis Function, Z-Score Index, Drought
Fields of Science
Citation
WoS Q
Scopus Q

OpenCitations Citation Count
N/A
Source
21st International Symposium on Production Research (ISPR) - Digitizing Production System
Volume
Issue
Start Page
689
End Page
701
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Citations
Scopus : 0
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