Sahin M.,Siirt State University
Advances in Space Research | Year: 2012
The aim of this research was to forecast monthly mean air temperature based on remote sensing and artificial neural network (ANN) data by using twenty cities over Turkey. ANN contained an input layer, hidden layer and an output layer. While city, month, altitude, latitude, longitude, monthly mean land surface temperatures were chosen as inputs, and monthly mean air temperature was chosen as output for network. Levenberg-Marquardt (LM) learning algorithms and tansig, logsig and linear transfer functions were used in the network. The data of Turkish State Meteorological Service (TSMS) and Technological Research Council of Turkey-Bilten for the period from 1995 to 2004 were chosen as training when the data of 2005 year were being used as test. Result of research was evaluated according to statistical rules. The best linear correlation coefficient (R), and root mean squared error (RMSE) between the estimated and measured values for monthly mean air temperature with ANN and remote sensing method were found to be 0.991-1.254 K, respectively. © 2012 COSPAR. Published by Elsevier Ltd. All rights reserved.
Akcan N.,Siirt State University
African Journal of Biotechnology | Year: 2012
The various nutrients belonging to carbon, nitrogen and amino acid sources, were investigated in terms of their effect on the production of extracellular protease by Bacillus licheniformis ATCC 12759. Comparison with the control in media containing all the simple sugars resulted in a decrease in proteolytic activity, while there was significant increase in enzyme yield in the case of the supplementation complex carbon source such as wheat flour and rice flour. Urea and sodium nitrate were the best organic and inorganic nitrogen sources, respectively. Among the amino acid sources tested, L-phenylalanine, L-cysteine, glycine and L-valine favored the production, respectively. FeSO 4, ZnSO 4 and CuSO 4 completely repressed protease production. Maximum protease production (10738.2±44.2 U/mg) was obtained in a medium containing 0.1% MgSO 4 in 24 h 37°C. © 2012 Academic Journals.
Balbay A.,Siirt State University
Energy Education Science and Technology Part A: Energy Science and Research | Year: 2012
In this study, the drying characteristics of bittims (Pistacia terebinthus) grown in Siirt, Turkey were investigated by using a temperature controlled microwave dryer system. Natural outer shells unpeeled and peeled bittims were used. The initial moisture content (MC) of samples was determined by oven drying at a temperature of 130 °C about 6 hours. The drying experiments were conducted at three different temperatures (35, 40 and 45 °C), air flow rates (FRs) (0.4, 0.55 and 0.7 m 3/h) and microwave power (250 W, 500 W and 750 W). The fit quality of models was evaluated using the determination coefficient, chi-square and root mean square error. Among the selected models, the Modified Henderson and Pabis et al model was found to be the best model for describing the drying characteristics of bittims. © Sila Science.
Temperature distributions in pavement and bridge slabs heated by using vertical ground-source heat pump systems [Distribuições de temperatura no pavimento e ponte lajes aquecidas usando um sistema vertical de bomba de calor derivado da terra]
Balbay A.,Siirt State University |
Esen M.,Firat University
Acta Scientiarum - Technology | Year: 2013
Temperature distribution which occurs in pavement and bridge slabs heated for de-icing and snow melting during cold periods is determined by using vertical ground-source heat pump (GSHP) systems with U-tube ground heat exchanger (GHE). The bridge and pavement models (slabs) for de-icing and snow melting were constructed. A three-dimensional finite element model (FEM) was developed to simulate temperature distribution of bridge slab (BS) and pavement slab (PS). The temperature distribution simulations of PS and BS were conducted numerically by computational fluid dynamics (CFD) program named 'Fluent'. Congruence between the simulations and experimental data was determined.
Sahin M.,Siirt State University
International Journal of Remote Sensing | Year: 2013
In this study, solar radiation (SR) is estimated at 61 locations with varying climatic conditions using the artificial neural network (ANN) and extreme learning machine (ELM). While the ANN and ELM methods are trained with data for the years 2002 and 2003, the accuracy of these methods was tested with data for 2004. The values for month, altitude, latitude, longitude, and land-surface temperature (LST) obtained from the data of the National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA-AVHRR) satellite are chosen as input in developing the ANN and ELM models. SR is found to be the output in modelling of the methods. Results are then compared with meteorological values by statistical methods. Using ANN, the determination coefficient (R2), mean bias error (MBE), root mean square error (RMSE), and Willmott's index (WI) values were calculated as 0.943, -0.148 MJ m-2, 1.604 MJ m-2, and 0.996, respectively. While R2 was 0.961, MBE, RMSE, and WI were found to be in the order 0.045 MJ m-2, 0.672 MJ m-2, and 0.997 by ELM. As can be understood from the statistics, ELM is clearly more successful than ANN in SR estimation. © 2013 Copyright Taylor & Francis.