Drought Modelling Using Artificial Intelligence Algorithms in Izmir District
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Date
2022
Authors
Zeynep İrem Özen
Berk Sadettin Tengerlek
Damla Yüksel
Efthimia Staiou
Mir Jafar Sadegh Safari
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Springer Science and Business Media Deutschland GmbH
Open Access Color
Green Open Access
No
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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 Küçük Menderes basin and several developed models are compared according to the variety of statistical performance metrics. © 2022 Elsevier B.V. All rights reserved.
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Keywords
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, 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
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International Symposium for Production Research ISPR2021
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