International Journal of Advances in Scientific Research and Engineering, 2020
The dependence of agricultural production on water resources is a known fact. Therefore, understa... more The dependence of agricultural production on water resources is a known fact. Therefore, understanding hydrological processes and events in agricultural production form the basis of effective and reliable management of water resources. Many traditional methods used for the analysis of time-dependent variables in hydrology and meteorology assume linear relationships between these variables. However, the temporal changes of these parameters are quite complex and therefore cannot be easily modeled by conventional estimation methods. Artificial neural networks (ANNs), on the other hand, allow the analysis of nonlinear relationships or processes whose statistical or mathematical calculations cannot be determined in such systems. ANNs have been accepted as a successful model in multidimensional research involving dynamic processes in the field of hydrology.
This study, it is aimed at the modeling of stream flows in Bektas Creek. With the aim of modeling, daily meteorological parameters (precipitation, temperature, sunbathing time, relative humidity) measured in Kangal Region and one day delayed flows were used. Streamflow forecasts are simulated with the Generalized Regression Neural Network (GRNN). To reveal the difference of the GRNN model from other ANNs, the same data were also used in the feed-forward neural network (FFNN) model. Model performances were evaluated taking into account the Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Determination coefficient (R).
Constructed wetland technology is used as an alternative of conventional wastewater treatment sys... more Constructed wetland technology is used as an alternative of conventional wastewater treatment systems in various parts of the world with low land costs and limited labor supply. In Turkey, constructed wetlands play a key role in economic and sustainable solution of wastewater problems especially of rural sections of the country. Domestic wastewater generally generate pollution through basic plant nutrients (nitrogen and phosphorus). Substrate adsorption is the principle phosphorus removal mechanism in constructed wetland systems. This study was conducted to investigate the phosphorus removal performance of zeolite and pumice minerals from domestic wastewaters. Filter column tests were conducted under laboratory conditions to assess the phosphorus removal performance of substrate materials. Zeolite and pumice materials were used alone and in mixtures (%(v/v) 75, 50 and 25) and filter columns were subjected to three different phosphorus concentrations (10, 20 and 40 mg-l) and four different hydraulic retention times (1, 2, 3 and 4 days). At the end of hydraulic retention times, effluent samples were taken, and EC, pH and TP tests were conducted on samples. The lowest variations in EC values were seen in pumice material. Increasing pH values were observed with increasing influent concentrations in all materials. Pumice exhibited greater phosphorus adsorption performance than zeolite and increasing phosphorus adsorption was observed with increasing pumice ratio in mixtures. Key Words: Natural treatment, substrate material, filtration, adsorption
International Journal of Advances in Scientific Research and Engineering, 2020
The dependence of agricultural production on water resources is a known fact. Therefore, understa... more The dependence of agricultural production on water resources is a known fact. Therefore, understanding hydrological processes and events in agricultural production form the basis of effective and reliable management of water resources. Many traditional methods used for the analysis of time-dependent variables in hydrology and meteorology assume linear relationships between these variables. However, the temporal changes of these parameters are quite complex and therefore cannot be easily modeled by conventional estimation methods. Artificial neural networks (ANNs), on the other hand, allow the analysis of nonlinear relationships or processes whose statistical or mathematical calculations cannot be determined in such systems. ANNs have been accepted as a successful model in multidimensional research involving dynamic processes in the field of hydrology.
This study, it is aimed at the modeling of stream flows in Bektas Creek. With the aim of modeling, daily meteorological parameters (precipitation, temperature, sunbathing time, relative humidity) measured in Kangal Region and one day delayed flows were used. Streamflow forecasts are simulated with the Generalized Regression Neural Network (GRNN). To reveal the difference of the GRNN model from other ANNs, the same data were also used in the feed-forward neural network (FFNN) model. Model performances were evaluated taking into account the Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Determination coefficient (R).
Constructed wetland technology is used as an alternative of conventional wastewater treatment sys... more Constructed wetland technology is used as an alternative of conventional wastewater treatment systems in various parts of the world with low land costs and limited labor supply. In Turkey, constructed wetlands play a key role in economic and sustainable solution of wastewater problems especially of rural sections of the country. Domestic wastewater generally generate pollution through basic plant nutrients (nitrogen and phosphorus). Substrate adsorption is the principle phosphorus removal mechanism in constructed wetland systems. This study was conducted to investigate the phosphorus removal performance of zeolite and pumice minerals from domestic wastewaters. Filter column tests were conducted under laboratory conditions to assess the phosphorus removal performance of substrate materials. Zeolite and pumice materials were used alone and in mixtures (%(v/v) 75, 50 and 25) and filter columns were subjected to three different phosphorus concentrations (10, 20 and 40 mg-l) and four different hydraulic retention times (1, 2, 3 and 4 days). At the end of hydraulic retention times, effluent samples were taken, and EC, pH and TP tests were conducted on samples. The lowest variations in EC values were seen in pumice material. Increasing pH values were observed with increasing influent concentrations in all materials. Pumice exhibited greater phosphorus adsorption performance than zeolite and increasing phosphorus adsorption was observed with increasing pumice ratio in mixtures. Key Words: Natural treatment, substrate material, filtration, adsorption
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Papers by FATMA AKÇAKOCA
This study, it is aimed at the modeling of stream flows in Bektas Creek. With the aim of modeling, daily meteorological parameters (precipitation, temperature, sunbathing time, relative humidity) measured in Kangal Region and one day delayed flows were used. Streamflow forecasts are simulated with the Generalized Regression Neural Network (GRNN). To reveal the difference of the GRNN model from other ANNs, the same data were also used in the feed-forward neural network (FFNN) model. Model performances were evaluated taking into account the Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Determination coefficient (R).
Keywords: Meteorological Parameters, Streamflow Prediction, Generalized Regression Neural Network, Feed-Forward Neural Network.
Key Words: Natural treatment, substrate material, filtration, adsorption
This study, it is aimed at the modeling of stream flows in Bektas Creek. With the aim of modeling, daily meteorological parameters (precipitation, temperature, sunbathing time, relative humidity) measured in Kangal Region and one day delayed flows were used. Streamflow forecasts are simulated with the Generalized Regression Neural Network (GRNN). To reveal the difference of the GRNN model from other ANNs, the same data were also used in the feed-forward neural network (FFNN) model. Model performances were evaluated taking into account the Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Determination coefficient (R).
Keywords: Meteorological Parameters, Streamflow Prediction, Generalized Regression Neural Network, Feed-Forward Neural Network.
Key Words: Natural treatment, substrate material, filtration, adsorption