2014
DOI: 10.1016/j.jhydrol.2014.10.040
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A spatio-temporal hybrid neural network-Kriging model for groundwater level simulation

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Cited by 89 publications
(26 citation statements)
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“…Despite the importance of groundwater as a component of the hydrological cycle and source of potable water, especially in arid and semiarid regions, there is a paucity of studies of long‐term groundwater fluctuations. One of the main reasons for this is that long‐term series observation data of groundwater depth are unavailable, and it is difficult to elucidate the rules of groundwater depth by using short‐term data (Dogrul et al, ; Jackson et al, ; Tapoglou et al, ). In this study, based on the significant correlation between groundwater depth and plant growth, we reconstructed groundwater depths during the past 66 years (1950–2015 CE) by using tree rings of P. euphratica in the lower reaches of the Tarim River (Figure b).…”
Section: Discussionmentioning
confidence: 99%
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“…Despite the importance of groundwater as a component of the hydrological cycle and source of potable water, especially in arid and semiarid regions, there is a paucity of studies of long‐term groundwater fluctuations. One of the main reasons for this is that long‐term series observation data of groundwater depth are unavailable, and it is difficult to elucidate the rules of groundwater depth by using short‐term data (Dogrul et al, ; Jackson et al, ; Tapoglou et al, ). In this study, based on the significant correlation between groundwater depth and plant growth, we reconstructed groundwater depths during the past 66 years (1950–2015 CE) by using tree rings of P. euphratica in the lower reaches of the Tarim River (Figure b).…”
Section: Discussionmentioning
confidence: 99%
“…Moreover, the longest records of groundwater level histories measured by instruments are not more than 150 years in Europe and the United States (Liu, Gou, Zhang, & Chen, ). Consequently, it is necessary to reconstruct groundwater levels by utilizing a variety of methods to lengthen relatively short observational records (Dogrul, Kadir, Brush, & Chung, ; Herrmann, Baghdadi, & Blaschek, ; Jackson et al, ; Singh, Eldho, & Kumar, ; Tapoglou, Karatzas, Trichakis, & Varouchakis, ).…”
Section: Introductionmentioning
confidence: 99%
“…All the spatial maps were prepared using ArcGIS 10.1 with Ordinary kriging (OK) model. This model is widely accepted and is employed in several pieces of research ( [41][42][43]. The Geochemist's Workbench was employed to understand the hydrochemical facies in the groundwater.…”
Section: Methodsmentioning
confidence: 99%
“…It has been noted that ANFIS (Takagi and M. Sugeno, 1985;Jang, 1993Jang, , 1995Jang, , 1996Celikyilmaz and Turksen, 2009;Wang et al, 2009) exhibits better simulation performances than classical artificial neural networks (Nayak et al, 2004;El-Shafie et al, 2007;Firat, 2008;Pai et al, 2009;Wang et al, 2009;Maier et al, 2010). Moreover, ANFIS has already been successfully used to interpolate hydraulic head distribution (Lin and Chen, 2004;Kholghi and Hosseini, 2009;Flipo and Kurtulus, 2011;Kurtulus et al, 2011;Kurtulus and Flipo, 2012;Tapoglou et al, 2014).…”
Section: Introductionmentioning
confidence: 99%