Development of hydropower energy in two adjacent basins (northeast of turkey)

The main objective in doing the present study is to investigate the sustainable development ofhydropower plants in two adjacent basins being located in northeast of Turkey, which are the Coruhriver basin being the least problem river of Turkey in respect to international cooperation as comparedwith Turkey's other trans-boundary waters and the Eastern Black Sea Basin (EBSB) having greatadvantages from the view point of small hydropower potential or hydropower potential without storage among 25 hydrological basins in Turkey. The contribution of the hydropower energy potentialin these basins to reconstruction of Turkey electricity structure is investigated and a comparison inbetween is carried out. Finally, it is found that the EBSB will be corresponded from 8.3% and 10.3%of nowadays total electricity energy production and net electricity consumption of Turkey, while Coruh river basin will provide 7.40% and 9.19% of total electric generation and electricity consumption of Turkey, respectively, after all hydropower projects within these basins are commissioned. In other words, one-fifth of Turkey's electricity consumption will be met from northeast of Turkey. For this reason, development studies and investments in the hydropower sector should be encouraged and supported and projects within these basins should be put into operation assoon as possible

Estimation of the monthly mean temperature values of the eastern black sea basin with statistical downscaling method using eraınterim re-analysis data

İstatistiksel ölçek indirgeme yöntemleri düşük çözünürlüğe sahip atmosferik değişkenler ile istasyonlardan ölçülmüş meteorolojik parametreler arasında istatistiksel ilişkiler kurulmasına dayanan yöntemlerdir. Bu çalışmada (0,75° x 0,75°) sayısal ağ çözünürlüğüne sahip EraInterim re-analiz veri setinde yer alan atmosferik değişkenler kullanılarak Doğu Karadeniz Havzası’nda ve çevresinde yer alan 12 meteoroloji istasyonundan ölçülmüş olan aylık ortalama sıcaklık parametresinin her bir istasyon için tahmin edilmesi amaçlanmıştır. Bu amaçla EraInterim re-analiz veri setinde yer alan yüzeysel parametrelerden yağış, sıcaklık, deniz yüzeyi basıncı ve yüzeysel hava basıncı ile 850, 500 ve 200 hPa basınç seviyelerindeki hava sıcaklığı, jeopotansiyel yükseklik ve rölatif nem atmosferik değişkenleri bağımsız değişkenler olarak kullanılmıştır. Meteoroloji istasyonlarından (1981-2010) döneminde ölçülmüş olan aylık ortalama sıcaklık değerleri ise bağımlı değişken olarak kullanılmıştır. Ölçek indirgeme yöntemi olarak çok değişkenli uyarlanabilir regresyon eğrileri (ÇDURE) yöntemi seçilmiştir. İstasyon temelinde kurulmuş olan ÇDURE model performanslarının değerlendirilmesi için ortalama karesel hatanın karekökü, saçılım indeksi, ortalama mutlak hata ve Nash-Sutcliffe (NS) etkinlik katsayısı istatistikleri kullanılmıştır. Hesaplanan NS değerinin tüm istasyonlar için 0.9-1.0 aralığında olduğu görülmüştür. Ayrıca EraInterim veri setinden seçilmiş olan küresel ölçekli değişkenlerin yerel ölçekteki sıcaklık değerleri tahmininde başarılı olduğu ortaya çıkmıştır. Bu sonuçlar ÇDURE istatistiksel ölçek indirgeme yönteminin kaba ölçekli atmosferik değişkenlerin bölgesel ölçeğe indirgenmesinde kullanılabilir olduğunu göstermiştirStatistical downscaling methods are based on determination of statistical relationships between low resolution atmospheric variables and measured climate parameters from meteorological stations. In this study, it was aimed to estimate the monthly mean temperature measured from 12 meteorological stations in and around the Eastern Black Sea Basin using atmospheric variables in the EraInterim re-analysis data set with grid resolution (0.75° x 0.75°). For this purpose, the variables of precipitation, temperature, sea surface pressure, surface air pressure and air temperature, geopotential height and relative humidity at 850, 500 and 200 hPa pressure levels in the EraInterim re-analysis data set were used as independent variables. Monthly mean temperature values measured from meteorological stations (1981-2010) were used as dependent variables. Multivariate adaptive regression splines (MARS) method selected as downscaling method. The root mean square error, scattering index, mean absolute error and Nash Sutcliffe (NS) efficiency coefficient statistics were used to evaluate the performance of the MARS model based on the station. The NS value calculated for all stations was in the range of 0.9-1.0. In addition, the global scale variables selected from the EraInterim data set were found to be quite successful in estimating local temperature values. The results obtained from the study showed that MARS statistical downscaling method can be used to downscale the coarse scale atmospheric variables to the regional scal

Prediction of maximum annual flood discharges using artificial neural network approaches

U radu se istražuje primjenjivost pristupa umjetnih neuronskih mreža (ANN) za određivanje maksimalnih godišnjih protoka. Uspoređuje se učinkovitost triju modela neuronskih mreža: višeslojne perceptronske neuronske mreže (MLP_NN), generalizirane neuronske mreže usmjerene prema naprijed (GFF_NN) i analiza osnovnih komponenata pomoću neuronskih mreža (PCA_NN). Predloženi pristupi primijenjeni su na 33 vodomjerne. Utvrđeno je da je optimalna metoda PCA_NN s tri skrivena sloja prikladnija za određivanje maksimalnih godišnjih protoka od optimalnih modela MLP_NN i GFF_NN.The applicability of artificial neural network (ANN) approaches for estimation of maximum annual flows is investigated in the paper. The performance of three neural network models is compared: multi layer perceptron neural networks (MLP_NN), generalized feed forward neural networks (GFF_NN), and principal component analysis with neural networks (PCA_NN). The proposed approaches were applied to 33 stream-gauging stations. It was found that the optimal 3-hidden layered PCA_NN method was more appropriate than the optimal MLP_NN and GFF_NN models for the estimation of maximum annual flows

Spatial forecasting of dissolved oxygen concentration in the Eastern Black Sea Basin, Turkey

The aim of this study was to model, as well as monitor and assess the surface water quality in the Eastern Black Sea (EBS) Basin stream, Turkey. The water-quality indicators monitored monthly for the seven streams were water temperature (WT), pH, total dissolved solids (TDS), and electrical conductivity (EC), as well as luminescent dissolved oxygen (LDO) concentration and saturation. Based on an 18-month data monitoring, the surface water quality variation was spatially and temporally evaluated with reference to the Turkish Surface Water Quality Regulation. First, the teaching learning based optimization (TLBO) algorithm and conventional regression analysis (CRA) were applied to three different regression forms, i.e., exponential, power, and linear functions, to predict LDO concentrations. Then, the multivariate adaptive regression splines (MARS) method was employed and three performance measures, namely, mean absolute error (MAE), root means square error (RMSE), and Nash Sutcliffe coefficient of efficiency (NSCE) were used to evaluate the performances of the MARS, TLBO, and CRA methods. The monitoring results revealed that all streams showed the same trend in that lower WT values in the winter months resulted in higher LDO concentrations, while higher WT values in summer led to lower LDO concentrations. Similarly, autumn, which presented the higher TDS concentrations brought about higher EC values, while spring, which presented the lower TDS concentrations gave rise to lower EC values. It was concluded that the water quality of the streams in the EBS basin was high-quality water in terms of the parameters monitored in situ, of which the LDO concentration varied from 9.13 to 10.12 mg/L in summer and from 12.31 to 13.26 mg/L in winter. When the prediction accuracies of the three models were compared, it was seen that the MARS method provided more successful results than the other methods. The results of the TLBO and the CRA methods were very close to each other. The RMSE, MAE, and NSCE values were 0.2599 mg/L, 0.2125 mg/L, and 0.9645, respectively, for the best MARS model, while these values were 0.4167 mg/L, 0.3068 mg/L, and 0.9086, respectively, for the best TLBO and CRA models. In general, the LDO concentration could be successfully predicted using the MARS method with various input combinations of WT, EC, and pH variables

Status of hydropower and water resources in the Southeastern Anatolia Project (GAP) of Turkey

This study focuses on the water resources development of the Southeastern Anatolia Project (GAP) in Turkey. The Euphrates and Tigris Rivers, located in the GAP, have the largest flow volume of 16.9% and 11.4% of Turkey’s average annually runoff (186 billion m3), respectively. While the Euphrates represents over 19.4% of the national gross hydropower potential (433 GWh/year), the Tigris correspond to 11.2% of this potential. 20,523 GWh/year of hydropower potential in the GAP is in operation. When all projects of the GAP in planning are completed, the total hydropower potential reaches a level of 27,419 GWh/year. This value corresponds to 10.9% of the annual electric energy production of Turkey in 2014 (251.96 GWh). There is also irrigable land of 1.06 million ha in the GAP, and now roughly 33.7%, 357,241 ha, of which have been irrigated

Development of hydropower energy in Turkey: The case of Çoruh river basin

The main objective in doing the present study is to investigate the sustainable development of hydropower plants in the Çoruh river basin of Turkey, which is least problem river of Turkey in respect to international cooperation as compared with Turkey's other trans-boundary waters. Initial studies concerning the hydropower production potential in Çoruh basin had been carried out by Turkish authorities in the late 1960s. Total installed power capacity and annual average energy generation of 37 dams and run of river (without storage) hydropower plants developed at various project stages by The Electrical Power Resources Survey and Development Administration (EIE) in Çoruh basin are 3132.70 MW and 10.55 TWh/yr, respectively. Today, this generation value corresponds 6.45% of Turkey's energy consumption in 2006 while it meets 6.3% of total electricity energy production of Turkey which is equal to 167.9 TWh/yr in 2006. Besides, this potential developed at various project stages in Çoruh basin will provide 24.1% of Turkey's hydroelectric energy generation being equal to 43.8 TWh/yr in 2006.Hydropower The Coruh river basin The Coruh Basin Development Plan

Geo-spatial multi-criteria evaluation of wave energy exploitation in a semi-enclosed sea

The present study aims to determine priority areas for installation of wave energy converters (WECs) in a semi-enclosed sea using a multi-criteria, spatial, decision-making analysis based on geographical information systems (GIS). The study also suggests a new methodology for determination of suitable areas for WECs taking into consideration different extreme wave conditions, intra-annual variation of wave conditions, and operational range of wave conditions by the WECs. A case study over a distance of 1140 km along the coast in the southwest Black Sea is presented. In the multi- criteria analysis, areas with environmental, economic, technical and social constraints are excluded. Ocean depth, distance to ports, shore, power line, and sub-station, wave climate, and sea-floor geology are all evaluated for their impact on the system implementation and weighted according to their relevance. Thus, the final suitability index (SI) map is produced and spatial statistical significance of the suitable areas is checked using hotspot analysis. Based on this, Kirklareli coastal area and the area between Igneada Cape and Kiyikoy village are determined as primary priority areas. The sustainability parameters with different weights proposed in this study do not differentiate priority areas but affect the SI scores. (C) 2020 Elsevier Ltd. All rights reserved

Artificial neural networks for estimation of temporal rate coefficient of equilibrium bar volume

45-55Present study consists the growth of a bar profile caused by cross-shore sediment transport. This is especially on growth of bar volume (V) toward equilibrium bar volume (Veq). Three analysis methods being a power and linear regression analysis (PRA and LRA) and an Artificial Neural Network (ANN) analysis were performed to determine empirical temporal rate coefficient (α). Forty-two experimental data were used for training set and the rest of the experimental data were used for testing set in the ANN analysis. As the results of analyses, the smallest average relative and root mean square error (RMSE) computed for the ANN methods are 7.578% and 0.029, respectively. It has been obtained that the ANN analysis, which is used for determination of α coefficient, gives reasonable results. Finally, bar volumes were calculated by means of computed α values and compared with the results of experimental data

Estimates of energy consumption in Turkey using neural networks with the teaching-learning-based optimization algorithm

The main objective of the present study was to apply the ANN (artificial neural network) model with the TLBO (teaching-learning-based optimization) algorithm to estimate energy consumption in Turkey. Gross domestic product, population, import, and export data were selected as independent variables in the model. Performances of the ANN-TLBO model and the classical back propagation-trained ANN model (ANN-BP (teaching learning-based optimization) model) were compared by using various error criteria to evaluate the model accuracy. Errors of the training and testing datasets showed that the ANN-TLBO model better predicted the energy consumption compared to the ANN-BP model. After determining the best configuration for the ANN-TLBO model, the energy consumption values for Turkey were predicted under three scenarios. The forecasted results were compared between scenarios and with projections by the MENR (Ministry of Energy and Natural Resources). Compared to the MENR projections, all of the analyzed scenarios gave lower estimates of energy consumption and predicted that Turkey's energy consumption would vary between 142.7 and 158.0 Mtoe (million tons of oil equivalent) in 2020

Forecasting daily streamflow discharges using various neural network models and training algorithms

WOS: 000441994400049Streamflow forecasting based on past records is an important issue in both hydrologic engineering and hydropower reservoir management. In the study, three artificial Neural Network (NN) models, namely NN with well-known multi-layer perceptron (MLPNN), NN with principal component analyses (PCA-NN), and NN with time lagged recurrent (TLR-NN), were used to 1, 3, 5, 7, and 14 ahead of daily streamflow forecast. Daily flow discharges of Haldizen River, located in the Eastern Black Sea Region, Turkey the time period of 1998-2009 was used to forecast discharges. Backpropagation (BP), Conjugate Gradient (CG), and Levenberg-Marquardt (LM) were applied to the models as training algorithm. The result demonstrated that, firstly, the forecast ability of CG algorithm much better than BP and LM algorithms in the models; secondly, the best performance was obtained by PCA-NN and MLP-NN for short time (1, 3, and 5 day-ahead) forecast and TLR-NN for long time (7 and 14 day-ahead) forecast