WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7150

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2016 - 20203

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Department: search.filters.department.İzmir Institute of Technology. City and Regional PlanningInstitution Author: search.filters.institutionAuthor.Tayfur, Gökmen

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Now showing 1 - 3 of 3
  • Article
    Citation - WoS: 8
    Citation - Scopus: 8
    Estimation Groundwater Total Recharge and Discharge Using Gis-Integrated Water Level Fluctuation Method: a Case Study From the Alasehir Alluvial Aquifer Western Anatolia, Turkey
    (Springer Verlag, 2020) Şimşek, Celalettin; Demirkesen, Ali Can; Baba, Alper; Kumanlıoğlu, Ahmet; Durukan, Seda; Aksoy, Niyazi; Tayfur, Gökmen
    The estimation of groundwater recharge is an essential process for hydrogeological study. Realistic determination approach is crucial for assessing groundwater potential in an aquifer system and estimating of groundwater levels and/or changes in dry periods. Based on these matters, we employ a GIS-integrated groundwater level fluctuation method to determine the groundwater recharge for a hydrological period in the Alasehir alluvial aquifer (W. Anatolia). The method basically takes into account both increasing and decreasing of the groundwater levels due to the recharge and discharge mechanisms in the aquifer. In this study, 16 pumping and monitoring wells were drilled with a total depth of 1300 m, and water level data loggers were installed into the monitoring wells to determine the groundwater level changes. The spatial distribution of the monthly groundwater level change map was multiplied by the aquifer storage distribution map and then the accurate water volume is calculated by using the 3-D spatial analysis. According to our evaluation in the aquifer, positive volume change of the groundwater is 187 hm(3) in a year, which is considered as a recharge value of groundwater. It is concluded that the GIS-integrated water table fluctuation method gave rise to estimate the total recharge amount of the groundwater in the Alasehir aquifer. The total groundwater recharge indicates that total inflow in the aquifer from precipitation, leakage from surface water and irrigation waters. It can be stated that the recharge estimation of groundwater in a surficial aquifer, like the Alasehir aquifer, is fairly easy using the GIS-integrated water table fluctuation method.
  • Article
    Citation - WoS: 16
    Citation - Scopus: 20
    Groundwater Recharge Estimation Using Hydrus 1d Model in Alaşehir Sub-Basin of Gediz Basin in Turkey
    (Springer Verlag, 2019) Tonkul, Serhat; Baba, Alper; Şimşek, Celalettin; Durukan, Seda; Demirkesen, Ali Can; Tayfur, Gökmen
    Gediz Basin, located in the western part of Turkey constituting 2% land of the country, has an important groundwater potential in the area. Alasehir sub-basin, located in the southeast of the Gediz Basin and subject to the extensive withdrawal for the irrigation, constitutes the study area. Natural recharge to the sub-basin due to precipitation is numerically investigated in this study. For this purpose, 25 research wells, whose depths range from 20 to 50 m, were drilled to observe the recharge and collect the necessary field data for the numerical model. Meteorological data were collected from 3 weather stations installed in the study area. The numerical model HYDRUS was calibrated using the field water content data. Soil characterization was done on the core samples; the aquifer characterization was performed, and the alluvial aquifer recharge due to precipitation was calculated. As a result, the computed recharge value ranges from 21.78 to 68.52 mm, with an average value of 43.09 mm. According to the numerical model, this amount of recharge corresponds to 10% of the amount of annual rainfall.
  • Article
    Citation - WoS: 2
    Citation - Scopus: 2
    Soft Computing and Regression Modelling Approaches for Link-Capacity Functions
    (Czech Technical University in Prague, 2016) Koşun, Çağlar; Tayfur, Gökmen; Çelik, Hüseyin Murat
    Link-capacity functions are the relationships between the fundamental traffic variables like travel time and the flow rate. These relationships are important inputs to the capacity-restrained traffic assignment models. This study investigates the prediction of travel time as a function of several variables V/C (flow rate/capacity), retail activity, parking, number of bus stops and link type. For this purpose, the necessary data collected in Izmir, Turkey are employed by Artificial Neural Networks (ANNs) and Regression-based models of multiple linear regression (MLR) and multiple non-linear regression (MNLR). In ANNs modelling, 70% of the whole dataset is randomly selected for the training, whereas the rest is utilized in testing the model. Similarly, the same training dataset is employed in obtaining the optimal values of the coefficients of the regression-based models. Although all of the variables are used in the input vector of the models to predict the travel time, the most significant independent variables are found to be V/C and retail activity. By considering these two significant input variables, ANNs predicted the travel time with the correlation coefficient R = 0:87 while this value was almost 0.60 for the regression-based models.