Yazarlar
- Katipoğlu, Okan Mert(75)
- Achite, Mohammed(21)
- Kartal, Veysi(15)
- Elshaboury, Nehal(12)
- Sarıgöl, Metin(12)
- Ertugay, Neşe(9)
- Katipoglu, Okan Mert(7)
- Pande, Chaitanya Baliram(6)
- Saroughi, Mohsen(6)
- Yavuz, Emre(6)
- Aktürk, Gaye(5)
- Caglar, Bulent(5)
- Elbeltagi, Ahmed(5)
- Gürses, Ahmet(5)
- Keblouti, Mehdi(5)
- Kilinc, Huseyin Cagan(5)
- Pandey, Kusum(5)
- Samui, Pijush(5)
- Acar, Reşat(4)
- Bazrafshan, Ommolbanin(4)
- Dalkilic, Huseyin Yildirim(4)
- Dalkılıç, Hüseyin Yıldırım(4)
- Emami, Somayeh(4)
- Ertugay, Nese(4)
- Jehanzaib, Muhammad(4)
- Kocakaplan, Nihal(4)
- Malkoç, Emine(4)
- Mirzania, Ehsan(4)
- Mohammadi, Babak(4)
- Simsek, Oguz(4)
- Abdallah, Mohammed(3)
- Bailek, Nadjem(3)
- Berhail, Sabri(3)
- El Nemr, Ahmed(3)
- Güner, Eda Keleş(3)
- Karakoyun, Erkan(3)
- Ozkan, Furkan(3)
- Santos, Celso Augusto Guimarães(3)
- Sökmen, Teoman Özgür(3)
- Uzun, Sinem(3)
Yayınlar
- ArticleYalnız künye2023Evaporation Prediction with Wavelet-Based Hyperparameter Optimized K-Nearest Neighbors and Extreme Gradient Boosting Algorithms in a Semi-Arid Environment
Katipoğlu, Okan Mert
The study aims to reveal which mother wavelet type performs best in evaporation prediction. This study used a hybrid algorithm that combined K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost) methods, and discrete wavelet transform to estimate the evaporation values in the summer months in Bursa. While establishing wavelet-machine learning models, Daubechies2, coiflet 2, symlet 2 and biortogonal 2.2 wavelets were obtained by separating various input variables into sub-signals. For the model’s setup, total precipitation, average temperature, average relative humidity, average wind speed, total sunshine duration values in the monthly time period were used as inputs. Total evaporation data were presented as output of the algorithms. The performances of the established models were evaluated with the coefficients of determination, root mean square error, mean absolute error and scatter diagrams. It has been determined that the optimum model is obtained with the hybrid algorithm established by the combination of biorthogonal 2.2 and KNN methods. The study results provide agricultural, meteorological, hydrological and environmental engineers with essential resources for planning and managing water-related issues.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2023Evaluation of the performance of data-driven approaches for filling monthly precipitation gaps in a semi-arid climate conditions
Katipoğlu, Okan Mert
Missing data cause problems in meteorological, hydrological, and climate analysis. The observation data should be complete and cover long periods to make the research more accurate and reliable. Artificial intelligence techniques have attracted interest for completing incomplete meteorological data in recent years. In this study the abilities of machine learning models, artificial neural networks, the nonlinear autoregressive with exogenous input (NARX) model, support vector regression, Gaussian processes regression, boosted tree, bagged tree (BAT), and linear regression to fill in missing precipitation data were investigated. In developing the machine learning model, 70% of the dataset was used for training, 15% for testing, and 15% for validation. The Bayburt, Tercan, and Zara precipitation stations, which are closest to the Erzincan station and have the highest correlation coefficients, were used to fill the data gaps. The accuracy of the constructed models was tested using various statistical criteria, such as root-mean-square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe model efficiency coefficient (NSE), and determination coefficient (R2) and graphical approaches such as scattering, box plots, violin plots, and Taylor diagrams. Based on the comparison of model results, it was concluded that the BAT model with R2: 0.79 and NSE: 0.79 and error (RMSE: 11.42, and MAE: 7.93) was the most successful in the completion of missing monthly precipitation data. The contribution of this research is assist in the choice of the best and most accurate method for estimating precipitation data in semi-arid regions like Erzincan.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2023Predicting hydrological droughts using ERA 5 reanalysis data and wavelet-based soft computing techniques
Katipoğlu, Okan Mert
The effective forecasting of droughts is the basic step of drought risk management, which consists of drought preparedness, mitigation, and early warning phases. This study used historical values of meteorological and hydrological drought indices to predict ERA 5 reanalysis data based on future hydrological droughts with a 1-month lead-time in Diyarbakır, in the Tigris basin. Regression tree (RT), boosted tree (BT), bagged tree (BAT), support vector machines (SVM), and adaptive neuro-fuzzy inference system (ANFIS) models and hybrid wavelet-machine learning models created by combining these models with discrete wavelet transform were analyzed to predict Standardized Runoff Index (SRI) based hydrological droughts. To construct the models and determine the temporal dependencies and relationships between variables, autocorrelation functions (ACF), partial autocorrelation functions (PACF), and correlation matrices were used. In addition, statistical criteria such as root mean square error (RMSE), mean absolute error (MAE) and, coefficients of determination (R 2), and the Taylor diagram were used to evaluate the success of the established models. The study has provided evidence that the application of wavelet transform can significantly improve the efficacy of hydrological drought prediction. The ANFIS model with Train RMSE: 0.65, MAE: 0.47, R 2: 0.49; Test RMSE: 0.84, MAE: 0.64, R 2: 0.39, which combines SRI (T − 1) and SRI (T) inputs, is considered the best stand-alone machine learning model for hydrological drought prediction. Furthermore, the most successful prediction performance is achieved by combining the SRI (T − 3), SRI (T − 2), SRI (T − 1), and SRI (T) inputs using the wavelet transform and BAT algorithm with Train RMSE: 0.59, MAE: 0.45, R 2: 0.59; Test RMSE: 0.74, MAE: 0.60, R 2: 0.48. Accordingly, it was concluded that hydrological droughts could be predicted using only previous hydrological droughts. Therefore, the study's outputs can be used to effectively plan water resources for decision-makers and various institutions in managing drought risk.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2023Boosting flood routing prediction performance through a hybrid approach using empirical mode decomposition and neural networks: a case study of the Mera River in Ankara
KatipoğLu, Okan Mert; Sarıgöl, Metin
Flood routing is vital in helping to reduce the impact of floods on people and communities by allowing timely and appropriate responses. In this study, the empirical mode decomposition (EMD) signal decomposition technique is combined with cascade forward backpropagation neural network (CFBNN) and feed-forward backpropagation neural network (FFBNN) machine learning (ML) techniques to model 2014 floods in Ankara, Mera River. The data are split in order to avoid the underfitting and overfitting problems of the algorithm. While establishing the algorithm, 70% of the data were divided into training, 15% testing and 15% validation. Graphical indicators and statistical parameters were used for the analysis of model performance. As a result, the EMD signal decomposition technique has been found to improve the performance of ML models. In addition, the EMD-FFBNN hybrid model showed the most accurate estimation results in the flood routing calculation. The study’s outputs can assist in designing flood control structures such as levees and dams to help reduce flood risk.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2023Simulation of the Potential Impacts of Projected Climate and Land Use Change on Runoff under CMIP6 Scenarios
Haider, Saif; Masood, Muhammad Umer; Rashid, Muhammad; Alshehri, Fahad; Pande, Chaitanya B.; Katipoğlu, Okan Mert; Costache, Romulus
Assessing the impacts of climate change and land use/land cover changes on water resources within a catchment is essential because it helps us understand how these dynamic factors affect the quantity, quality, and availability of freshwater. This knowledge is crucial for making informed decisions about water management, conservation, and adaptation strategies, especially in regions facing increasing environmental uncertainties and challenges to water resource sustainability. In Pakistan’s Kunhar River Basin (KRB), this investigation explores the potential effects of shifting land use/land cover (LULC), and climate on stream flows. The SWAT (Soil and Water Assessment Tool), a semi-distributed hydrological model, and the most recent Coupled Model Intercomparison Project phase 6 (CMIP6) dataset from multiple global climate models (GCMs) were used to evaluate these effects. The temperature and precipitation data were downscaled using the CMhyd software; for both shared socioeconomic pathways (SSP2 and SSP5), the top-performing GCM out of four was required to produce downscaled precipitation and temperature predictions while taking future land use characteristics into account. The output from the chosen GCM indicated that by the conclusion of the 21st century, relative to the reference period (1985–2014), the study area’s average monthly precipitation, highest temperature, and lowest temperature will be increasing. Precipitation is anticipated to increase between 2015 and 2100 by 20.5% and 29.1% according to the SSP2 and SSP5 scenarios, respectively. This study’s findings, which emphasize the need for project planners and managers taking into account the effects of climate and land cover changes in their management techniques, show that climate change can have a significant impact on the changing seasons of flows in the Kunhar River basin.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2023Removal of Cr6+ ions and mordant violet 40 dye from liquid media using Pterocladia capillacea red algae derived activated carbon-iron oxides
Mohamed, Soha Mahrous Ismail; Güner, Eda Keleş; Yılmaz, Murat; El Nemr, Ahmed
In recent years, water pollution has become one of the most dangerous problems facing the world. Pollution of water with heavy metals and different dyes has caused many harmful effects on human health, living organisms and our environment. In this study, iron oxide nanomagnetic composite from Pterocladia Capillacea red algae-derived activated carbon (PCAC-IO) was synthesized by co-precipitation method using different iron salts and different base solutions. The synthesized nanocomposite was investigated with various characterization techniques such as FTIR, BET, SEM-EDX, TEM, XRD, and VSM. The obtained PCAC-IO adsorbent was used for Cr6+ ions and Mordant Violet 40 (MV40) dye removal. The adsorption mechanism of Cr6+ ions and MV40 dye on PCAC-IO was examined using several adsorption and kinetic isotherm models. Langmuir and Freundlich models were investigated using experimental data. Pseudo-first-order (PFO), Pseudo-second-order (PSO) and intraparticle diffusion models (IPDM) were applied to identify the adsorption mechanism. It has shown that the PSO kinetic model fits better with the experimental data obtained from PCAC-IO. This result can be interpreted as the adsorption of the adsorbate on the nanocomposite as chemical adsorption. The optimum conditions for maximum Cr6+ ions removal (96.88%) with PCAC-IO adsorbent occur at room temperature, 5 g L−1 adsorbent concentration, 100 mg L−1 initial pollutant concentration, pH 1 and at the end of 180 min, while maximum MV40 dye removal (99.76%), other conditions being the same, unlikely it occurred at pH 2.06 and after 45 min. The most suitable model for Cr6+ ions removal under the conditions of 1 L−1 g adsorbent concentration and 400 mg L−1 adsorbate concentration was Langmuir (Qmax = 151.52 mg g−1), while for MV40 removal it was Freundlich (Qmax = 303.03 mg g−1). We propose the use of activated carbon-supported iron oxide prepared from bio-waste material, especially from Pterocladia Capillacea red algae, as a promising adsorbent with high efficiency in the removal of Cr6+ ions and MV40 dye from aqueous media.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2022Dicle Havzasındaki Bazı İstasyonlara ait Potansiyel Evapotranspirasyon Verilerinin Trend Analizi
Katipoğlu, Okan Mert
Potansiyel evapotranspirasyon (PET) değerlerindeki eğilimlerin belirlenmesi, iklim değişikliği ve kuraklık yönetimi, tarımsal su kaynaklarının daha verimli yönetimi ve sulama planlamasının optimal tasarımı konuları açısından büyük öneme sahiptir. Bu çalışmada Dicle havzasında bulunan Batman, Cizre, Diyarbakır ve Hakkâri meteoroloji istasyonları için, 1964-2017 yılları arasında hesaplanan mevsimsel ve yıllık PET değerlerinin trend analizleri gerçekleştirilmiştir. Trendlerin belirlenmesinde Şen in Yenilikçi Trend Analizi (ITA) ve Mann-Kendall Mertebe Korelasyon (MKMK) metotları kullanılmıştır. PET değerlerinin hesaplanmasında sıklıkla kullanılan, basit ve az veri gerektiren Thornthwaite metodolojisi tercih edilmiştir. Araştırma sonunda ITA yöntemine göre, havzada seçilen istasyonların PET verilerinde genelde istatistik açıdan anlamlı artış trendleri hakimken, Diyarbakır istasyonu için ilkbahar ve kış mevsimlerinde istatistik açıdan anlamlı azalan trendler tespit edilmiştir. MKMK testine göre, kış mevsimi dışında istasyon konumlarında PET değerlerinde anlamlı artışlar vardır. Ayrıca ITA ve MKMK testleri büyük ölçüde örtüşmekle birlikte bazı istasyonlarda ITA ile tespit edilen artış trendleri MKMK testi ile saptanamamıştır. Elde edilen sonuçlar tarımsal su kaynaklarının planlanması ve verimli bir şekilde kullanılmasında, küresel ısınmanın etkileriyle mücadele etmek için adaptasyon ve azaltma stratejileri geliştirilmesinde karar vericilere önemli bilgi temin etmektedir.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2022TiO2 Nanoparçacık Kaplı Pamuklu Kumaşın Fotokatalitik ve Antibakteriyel Özelliklerinin İncelenmesi
Kuzucu, Mehmet; Caglar, Bulent; Güner, Eda Keles; Doğan, Bilge; Özdemir, Agah Oktay
Bu çalışmada, fotokatalitik ve antibakteriyel özelliklere sahip, TiCl4'ün hidroliziyle sentezlenen farklı miktarlarda TiO2 nanoparçacıklar ile yüzey modifiye edilmiş pamuklu kumaşlar hazırlandı. Hazırlanan bu TiO2 nanoparçacıkları kaplı pamuklu kumaşların fotokatalitik aktiviteleri UVA ışınları altında Kristal Viyolet (KV) boyar maddesinin bozunmasında incelendi. KV’nin, TiO2 nanoparçacıkları kaplı pamuklu kumaşlar üzerindeki fotokatalitik bozunması için en uygun şartlar 0,625 mgL-1 başlangıç KV konsantrasyonu ve 57 mgL-1 TiO2 nanoparçacık yükleme miktarı olarak belirlendi. Bu en uygun şartlar altında, ilgili fotokatalizör varlığında 40 dakika içerisinde UVA ışınları altında KV’nin % 89,31’inin bozunmaya uğradığı tespit edildi ve bozunma reaksiyonunun birinci dereceden kinetiği takip ettiği belirlendi. Reaktif tür deneyleri, hidroksil radikallerinin diğer radikal türlere nispeten daha önemli bir rol oynadığını ortaya çıkardı ve elde edilen bu verilere göre olası bozunma mekanizması önerildi. Ayrıca TiO2 kaplanmış pamuklu kumaşların antibakteriyel aktivitesinin incelenmesi için disk difüzyon metodu kullanıldı. Antibakteriyel aktivite tayininde Gram pozitif bakteri olarak Staphylococcus aureus (ATCC 6538), Gram negatif bakteri olarak Escherichia coli (ATCC 25922) üzerinde denemeler gerçekleştirildi. Disk difüzyon deneyi sonuçlarına göre; 38 ve 57 mgL-1 konsantrasyonlarında TiO2 ile kaplanmış pamuklu kumaşlarda, antibiyotik referansların oluşturduğu inhibisyon zonu çapına kıyasla relatif olarak E. coli’ye karşı sırasıyla % 40,9 ve % 60,2 oranlarında antibakteriyel etki görülmüştür.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2022Monthly stream flows estimation in the karasu river of euphrates basin with artificial neural networks approach
Katipoğlu, Okan Mert
This study aims to estimate streamflow values with artificial neural networks (ANN) using various meteorological parameters. In developing the ANN model, various combinations of precipitation, air temperatures, and potential evapotranspiration values were used as inputs, and streamflow values were obtained. Meteorological data is divided into 70% train, 15% test, and 15% validation. In the model's design, various training algorithms, network architecture, input combinations, and the number of iterations were tried, and the most suitable model was tested. Correlation coefficient (R), coefficient of determination (R2), absolute error (AE), and absolute relative error (ARE) coefficients were compared, and the most suitable model was selected. According to the analysis results, the optimal model was obtained using 2000 iterations, the architecture of the 4-4-1 model, and the Quasi-Newton algorithm. It was determined that the ANNs successfully modeled the rainfall-runoff relationship and produced reliable estimates. In addition, it was revealed that the inclusion of potential evapotranspiration values obtained by the Thornthwaite method into the model increases the model's success.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2022Yeşilırmak havzasındaki hidrolojik kuraklıkların Mann-Kendall ve Yenilikçi Şen yöntemi ile trend analizi
Dalkılıç, Hüseyin Yıldırım; Katipoğlu, Okan Mert; Yeşilyurt, Sefa Nur
Bu çalışmada Yeşilırmak havzasında bulunan 7 adet akım gözlem istasyonuna ait aylık ve yıllık zaman periyodundaki akım kuraklık indekslerinin (AKİ) trend analizi yapılmıştır. Kuraklık trendlerinin ortaya çıkarılmasında Mann-Kendall (MK), Yenilikçi Şen Yöntemi (YŞY) ve Thiel Şen Yaklaşımı (TŞY) kullanılmıştır. İçsel bağımlılığı bulunan AKİ serilerine MK testi uygulanmadan önce trend-free pre-whitening (ön arındırma) işlemi ile serisel bağımlılığın etkisi kaldırılmıştır. Trend analizleri istatistiksel açıdan %1 ve %5 anlamlılık seviyelerine göre değerlendirilmiştir. Sonuç olarak hidrolojik kuraklık trendlerinin değişimini istenilen aralıkta grafiksel olarak göstermesi ve daha hassas olarak trendleri ifade edebilmesi açısından YŞY’nin MK testine göre üstün olduğu belirlenmiştir. Ayrıca çalışma alanında aylık ve yıllık zaman periyotlarında artan hidrolojik kuraklık trendlerinin hâkim olduğu tespit edilmiştir. Bu nedenle kuraklık risklerinin yönetilmesi, kuraklığa karşı alınabilecek önlemler ve erken uyarı sistemleri gibi aksiyonların bütünleşik bir şekilde uygulanması gerekmektedir.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2022Türkiye'de Ankara ili için düz plaka kollektör güneş sıcak su sisteminin TRNSYS yazılımı ile incelenmesi
Uzun, Sinem; Seyhan, Aslıhan Kurnuç; Coşkun, Salih
Düz plaka kollektör kullanan bir güneş enerjili su ısıtma sistemi, Türkiye'deki Ankara ili için, bir konut biriminin kullanım sıcak suyu ihtiyacı için modellenmiştir. Gerekli tasarım parametreleri belirlenmiş ve analizler TRNSYS simülasyon programı kullanılarak gerçekleştirilmiştir. Modellenen sistemde kullanım suyu sıcaklığı 55 °C olarak belirlenmiş ve kullanım yoğunluğuna göre gün içerisinde farklı debilerde su çekimleri gerçekleştirilmiştir. Su çekim hızları 07.00-09.00 saatleri arasında 80 kg/saat, 12.00-13.00 saatleri arasında 100 kg/saat ve 17.00-22.00 saatleri arasında 50 kg/saat olarak belirlenmiştir. Elde edilen sonuçlara göre tasarlanan sistem, yardımcı ısıtıcılar vasıtasıyla güneş enerjisi kullanılarak yaz ve kış aylarındaki sıcak su ihtiyacını karşılayabilmiştir. Kolektör verimi Eylül ayında %40 ile en yüksek değerine ulaşırken, Aralık ayında %8 ile en düşük verim değerine ulaşmıştır.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2022Ni-Zn metal-organic framework based membranes for rejection of Pb (II) ions
Senol-Arslan, Dilek; Gül, Ayse; Uzal, Nigmet; Yavuz, Emre
The present paper describes a sustainable and affordable supply of clean, and safe water approach to investigate Ni-Zn MOF embedded membrane for rejection of Pb (II) ions in aqueous solution. Ni-Zn MOF powder was prepared by solvo-thermal method, and then the Ni-Zn MOF embedded membranes with various concentrations (0.025 wt%, 0.05 wt% and 0.1 wt%). The membranes were fabricated by a common phase inversion method. Phase solutions of Pb(II) prepared with PVP at different concentrations in aqueous solutions. Water filtration and Pb (II) rejection tests were operated on a batch scale. The prepared Ni-Zn MOF powder characterized by FTIR, SEM-EDX analyses and zeta potential measurements. Ni-Zn MOF membranes were characterized by FTIR, SEM-EDX analysis, contact angle, and water permeability measurements. The effects of important parameters on adsorption including concentration and pH were investigated. The obtained results indicated that the maximum rejection of Pb(II) was 98% for a feed solution containing 80 mg Pb/L at pH 8 and assistance with 2 % PVP for 0.05 wt% Ni-Zn MOF membrane. Additionally, it was detected that blend membranes revealed better Pb(II) rejection than pure PSF membrane.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2022Spatial analysis of seasonal precipitation using various interpolation methods in the Euphrates basin, Turkey
Katipoğlu, Okan Mert
It is vital to accurately map the spatial distribution of precipitation, which is widely used in many fields such as hydrology, climatology, meteorology, ecology, and agriculture. This study aimed to reveal the spatial distribution of seasonal, long-term average precipitation in the Euphrates Basin with various interpolation methods. For this reason, Simple Kriging, Ordinary Kriging, Universal Kriging, Ordinary CoKriging, Empirical Bayesian Kriging, Radial Basis Functions (Completely Regularized Spline, Thin Plate Spline, Multiquadratic, Inverse Multiquadratic, Spline with Tensor), Local Polynomial Interpolation, Global Polynomial Interpolation, and Inverse Distance Weighting methods have been applied in the Geographical Information Systems environment. Long-term seasonal precipitation averages between 1966 and 2017 are presented as input for predicting precipitation maps. The accuracy of the precipitation prediction maps was based on linear regression analysis, root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (R), and determination coefficient (R2) values obtained from the cross-validation tests. The most suitable method was chosen for the interpolation method that gives the lowest RMSE, MAE, and the largest R and R2. As a result of the study, Ordinary CoKriging in spring and winter precipitation, Local Polynomial Interpolation in summer precipitation, and Ordinary Kriging in autumn precipitation were the most appropriate estimation methods.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2022Analyzing the trend and change point in various meteorological variables in Bursa with various statistical and graphical methods
Katipoğlu, Okan Mert
Determining the trend in meteorological variables is vital in water resources planning, energy production, and the design of water structures. This study analyzed trends and years of change in seasonal and annual average, maximum, minimum temperature, maximum precipitation, average relative humidity, average wind speed, and sunshine duration values in Bursa. Innovative-Şen trend analysis was used to determine the trends, and the sequential Mann–Kendall method, standard normal homogeneity test, and Pettitt test were used to determine the year of change. In addition, meteorological variables were divided into three ranges as low, medium, and high, and the trends in these ranges were analyzed graphically with the ITA approach. The ITA tests showed statistically significant increases in average, minimum, and maximum temperatures and average wind speed in seasonal and annual periods. On the other hand, statistically significant decreasing trends were observed in the mean relative humidity and sum sunshine duration values in seasonal and annual periods. In addition, significant decreasing trends were observed in the 95% confidence interval in the summer and annual periods in the maximum precipitation values. According to the sequential Mann–Kendall method, there were changes in temperature values in 1995 and 2015, wind speed values in 1946, relative humidity values in 1975 and 1977, and sunshine duration values around 1996. This study’s outputs significantly contribute to planners and decision-makers in terms of managing water resources, drought, and flood risk, developing adaptation strategies against climate change and agricultural irrigation and crop production.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2022Flood Routing Calculation with ANN, SVM, GPR, and RTE Methods
Sarigöl, Metin; Yesilyurt, Sefa Nur
Flood routing analysis is of great importance in predicting floods and taking all necessary precautions in the area where the flood occurred. By applying different machine learning algorithms to historical flood data, the success of the established models in flood routing calculations has been measured. In this study, flood hydrograph data on 05.05.2014 was passed through the training and testing stages using the Support Vector Machine (SVM), Gaussian Process Regression (GPR), Regression Tree Ensembles (RTE), and Artificial Neural Networks (ANN) methods, then routing calculations were made by applying the flood data on 03.06.2015 to these models. The results were compared with either ANN, SVM, GPR, RTE, or measured values. Root Mean Square Errors (RMSE) and Correlation Coefficient (R) values were calculated at the end of the comparative analysis. In conclusion, it has been determined that verifying the flood routing results performed by the SVM is the best result.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2022Analysis of spatial variation of temperature trends in the semiarid Euphrates basin using statistical approaches
Katipoğlu, Okan Mert
This study analyzes the spatial and temporal distribution of trends on monthly, seasonal, and annual mean temperatures (1967–2017) at 22 stations in the Euphrates Basin of Turkey. The recently proposed innovative trend analysis (ITA) and the nonparametric Mann–Kendall (MK) and Spearman’s rho (SR) test at 5% and 1% significance levels were applied to examine the temporal trends of temperatures. Before using the nonparametric trend analysis, the serial correlation of the temperature data was removed with the pre-whitening (PW) method. Then, MK-Z values were mapped in ArcGIS environment using the Empirical Bayesian Kriging (EBK) method to reveal the spatial variation of temperature trends. As a result, according to the ITA method, increasing temperature trends at 1% significance level dominate almost all periods. Based on the MK and SR tests, it was identified that increase trends were dominant at 1% and 5% significance levels in February, March, April, July, August, Spring, Summer, and Annual periods. According to the spatial temperature trend maps, significant increase trends occupy an important place in most of the basin in February, March, April, June, July, August, Spring, Summer, Winter, and Annual periods. The study results provide important information to water resources managers and decision-makers about the regions at significant risk in climate change in the Euphrates Basin.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2022Direct Blue 71 Degradation in the Presence of ZVI and H2 O2 Advanced Fenton Process: Determination of Optimum Conditions and Kinetics
Ertugay, Neşe; Acar, Filiz Nuran
This study investigated color and COD removal of azo dye Direct Blue 71 (DB71) by a Fenton-like process in zero-valent iron (Fe0 = ZVI). The UV–vis spectra of DB71 exhibit three main absorption bands – two in the UV region and one in the visible region. UV–vis spectra showed that the azo linkages in the molecule structure of the dyes were destroyed during the decolorization. The influence of various operational parameters such as pH, ZVI dosages, hydrogen peroxide (H2 O2) concentrations and temperature are studied to determine the optimum conditions for color and COD removal. pH is highly effective parameter of decolorization and COD destruction in this work. However, H2 O2 concentration and ZVI dosages were not only effective in the removal of COD for 100 mgL-1 initial DB71 concentration. Therefore, the optimum ZVI:H2 O2 ratio is based on determining the value of COD removal. Optimum conditions were accepted to be pH of 2.5, ZVI:H2 O2 ratio 4:1 and 20ºC temperature. The result indicated that 100% of decolorization efficiency was achieved within 20 min in the range of 150-400 mg/L initial DB71 concentrations by using optimal conditions. Residual COD values were 15.38, 22.35, 22.58, 27.04, 31.86 mg/L for 150, 200, 250, 300, 400 mg/L initial DB71 concentrations at the end of reaction time, respectively. Additionally, increasing the reaction temperature from 20 to 40ºC negatively affected the decolorization and degradation rate DB71. The experimental kinetic data are fitted very well the Behnajady–Modirshahla–Ghanbery model for color removal of DB71 dye.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2022Space-time variations of hydrological drought severities and trends in the semi-arid Euphrates Basin, Turkey
Katipoğlu, Okan Mert; Acar, Reşat
The main purpose of this study is to map the spatio-temporal variation of hydrological drought severities in the Euphrates Basin by using Kriging, Radial Based Function (RBF), Inverse Distance Weighting (IDW), Local Polynomial Interpolation (LPI), and Global Polynomial Interpolation (GPI) methods and to determine the distribution of hydrological drought trends. For this purpose, 18 streamflow observation stations and grid-based ERA5 reanalysis data were employed in and around the Euphrates Basin. The standardized runoff index (SRI) was calculated on monthly, 3-month, and 12-month time scales to determine hydrological droughts. Hydrological drought trends were determined by Mann Kendall (MK) and Modified Mann Kendall (MMK) tests. According to SRI, spatial-temporal distributions of droughts were mapped by Kriging, RBF, IDW, GPI, and LPI methods in the Geographical Information Systems environment. The LPI and Kriging method was selected as the most effective methods to predict hydrological droughts based on the cross-validation error. As a result of the trend analysis, the predominance of decreasing SRI trends in May (61.1%), June (72.2%), July (55.6%), August (61.1%) indicates that the basin is at risk of hydrological drought, especially in summer. According to the maps of spatial droughts, the widespread distribution of droughts in the basin indicated that the basin was greatly affected by hydrological droughts. For this reason, it has been emphasized that transboundary water effective management between riparian countries, planning of water structures, and effective implementation of the drought management plan is of great importance.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2021Determination of Meteorological and Hydrological Drought Maps with Various Interpolation Methods in the Euphrates Basin
Katipoğlu, Okan Mert; Acar, Resat
Drought is a natural disaster that adversely affects various sectors such as hydroelectric power generation, health, industry, tourism, economy, agriculture and animal husbandry. For this reason, monitoring, determining the spatio-temporal distribution, and managing drought risks by taking precautions are of great importance. In this study, it is aimed to map the spatio-temporal changes of various meteorological and hydrological droughts in the Euphrates Basin according to drought classes with Kriging, Radial Based Function (RBF) and Inverse Distance Weighting (IDW) interpolation methods and to evaluate the drought risk situation in the basin. For this, Standardized Precipitation Index, Statistical Z-Score Index, Precipitation Anomaly Index, Standardized Precipitation Evapotranspiration Index, Reconnaissance Drought Index and Standardized Runoff Index values were used for 16 Meteorology Observation Stations and 18 stream-gauging stations in the Euphrates Basin. As a result of the analyses, it has been determined that the Kriging method is the most effective method in predicting meteorological droughts in the basin, and RBF is the most effective method in predicting hydrological droughts. Since the drought risk level of the basin is determined to be high, it is recommended to make a drought management plan in the study area, to reduce negative drought effects, and to take early warnings and drought precautions.
RDL Kalıcı Kaydıİncele - ArticleAçık Erişim2021Determination of Some Heavy Metal Levels in Different Tissues of Common Carp (Cyprinus carpio, L., 1758) and Pike Barb (Luciobarbus esocinus, H., 1843) From Karasu River (Erzincan)
Mutlu, Cengiz; Bayır, İsmail
Heavy metals may be harmful to human health over the short and long term. Consumption of fish, one of the most important sources of protein in human life is one of the most common ways of heavy metal exposure. In this study for determine some heavy metals (Li, Na, Mg, Al, K, Ca, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Ag, Cd, Pb and U) concentration in different tissues as muscle, liver and gill of common carp (Cyprinus carpio, L., 1758) and pike barb (Luciobarbus esocinus, H., 1843) collected from Karasu River (Erzincan, Turkey) along the upper Euphrates Basin were collected between November 2019 and October 2020 on monthly. Heavy metal analysis was performed by ICP-MS -Bruker 820-MS. The results of heavy metals in tissues were compared to acceptable international limits. The studied metals were found at a considerable level in all seasons, indicating that the Karasu River biota is particularly vulnerable to heavy metal intake. Consequently, research has shown a strong connection between various elements and in addition, the human health risk assessment (HHRA) reveals are hazardous for consumption of fish.
RDL Kalıcı Kaydıİncele
