• Heidar Zarei

      Articles written in Journal of Earth System Science

    • Pre-processing data using wavelet transform and PCA based on support vector regression and gene expression programming for river flow simulation

      Abazar Solgi Amir Pourhaghi Ramin Bahmani Heidar Zarei

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      An accurate estimation of flow using different models is an issue for water resource researchers. In this study, support vector regression (SVR) and gene expression programming (GEP) models in daily and monthly scale were used in order to simulate Gamasiyab River flow in Nahavand, Iran. The results showed that although the performance of models in daily scale was acceptable and the result of SVR model was a little better, their performance in the daily scale was really better than the monthly scale. Therefore, wavelet transform was used and the main signal of every input was decomposed. Then, by using principal component analysis method, important sub-signals were recognized and used as inputs for the SVR and GEP models to produce wavelet-support vector regression (WSVR) and wavelet-gene expression programming. The results showed that the performance of WSVR was better than the SVR in such a way that the combination of SVR with wavelet could improve the determination coefficient of the model up to 3% and 18% for daily and monthly scales, respectively. Totally, it can be said that the combination of wavelet with SVR is a suitable tool for the prediction of Gamasiyab River flow in both daily and monthly scales.

    • Potential impacts of climate change on groundwater resources: A multi-regional modelling assessment

      Atie Hosseinizadeh Heidar Zarei Ali M Akhondali Hesam Seyedkaboli Babak Farjad

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      The complexity of understanding the groundwater resources in relation to climate change is caused by direct and indirect effects of climate change on hydrological processes. The study herein aims at implementing a physically based groundwater model to investigate the effects of climate change on groundwater system under 15 general circulation models (GCMs) in a semi-arid region from 2020 to 2044. A non-parametric probability density function estimator was used to quantify the level of uncertainties in the simulations. The method was applied to an area of 2073 km$^{2}$ in southwest Iran, consisting of five plains: western Dez, eastern Dez, Sabili, Deymche and Lor. The results indicate that there is a decline in the recharge in April, May, June and October. The range of changes in the recharge was determined to be between $-$10% and +13% in the Sabili plain, $-$6% and +10% in the Deymche plain, $-$4% and +10% in the western-Dez plain, $-$6% and +26% in the eastern-Dez plain, and $-$40% and +100% in the Lor plain. The most significant decline in the groundwater level occurred in the Sabili plain in September. The largest uncertainty in the simulation of recharge under GCM scenarios was determined to be in August, September and December. This study highlights that climate change can have a significant effect on groundwater resources in the region that reinforces the need for groundwater management plans and a long-term perspective.

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