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© 2022, The Author(s) under exclusive licence to Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences.This study aims to carry out regional intensity − duration − frequency (IDF) equality using the relationship with IDF obtained from point frequency analysis. Eleven empirical equations used in the literature for seven climate regions of Turkey were calibrated using particle swarm optimization (PSO) and genetic algorithm (GA) optimization techniques and the obtained results were compared. In addition, in this study, new regional IDF equations were obtained for each region utilizing Multi-Gene Genetic Programming (MGGP) method. Finally, Kruskal–Wallis (KW) test was applied to the IDF values obtained from the methods and the observed values. As a result of the study, it was observed that the coefficients of 11 empirical equations calibrated with PSO, and GA techniques were different from each other. The mean absolute error (MAE), root mean square error (RMSE), mean absolute relative error (MARE), coefficient of determination (R2), and Taylor diagram were used to evaluate the performances of PSO, GA, and MGGP techniques. According to the performance criteria, it has been determined that the IDF equations obtained by the MGGP method for the Eastern Anatolia, Aegean, Southeastern Anatolia, and Central Anatolia regions are more successful than the empirical equations calibrated with the PSO and GA method. The empirical IDF equations produced with PSO and the IDF equations acquired with MGGP have similar findings in the Mediterranean, Black Sea, and Marmara. In addition, the KW test results showed that the data of all models were from the same population.

Taşkınlar dünyada yaşanan en önemli afetlerden biri olup, Türkiye’de can ve mal kayıpları açısından depremlerden sonra ikinci, meteorolojik afetler arasında ilk sırada yer alır. Yaşamımızı, ekonomimizi etkileyen bu afetin yapısının araştırılması ve kayıpları en aza indirmek için önlemlerin alınması büyük önem arz etmektedir. Bu çalışmanın amacı yeterince akım ölçümü olmayan Samsun, Mert Irmağı için birim hidrograf yöntemlerini kullanarak taşkın tekerrür debilerinin elde edilmesi ve belli tekerrürlerdeki taşkın hidrograflarının çalışma alanında oluşturduğu taşkın yayılımının modellenmesidir. Taşkın tekerrür hidrograflarının belirlenmesinde yağış-akış modellemesine dayalı Mockus, Synder ve DSİ Sentetik yöntemleri kullanılmıştır. Yöntemler uygulanırken Mert Irmağı havzasını temsilen, havzayı kapsayan 3 adet meteoroloji istasyonunun (Samsun, Çakıralan, Mazlumoğlu) yağış verileri kullanılmıştır. Belli tekerrürlerdeki maksimum yağış verilerine en iyi uyum gösteren dağılım istatistiksel dağılımlar kullanılarak belirlenmiştir. Kullanılan istatistiksel dağılımlar Normal dağılım, Log-Normal dağılım, Gumbel, Log-Pearson, Pearson dağılımlarıdır. En uygun dağılım ise Kolmogorov-Smirnov uygunluk testi ile belirlenmiştir. Ardından elde edilen maksimum yağış yükseklikleri kullanılarak akım değerleri ve taşkın hidrografları elde edilmiştir. Böylece çalışmanın taşkın modelleme aşamasında geçilmiş ve elde edilen taşkın debileri ve hidrografı FLO-2D programı kullanılarak modellenmiştir. Taşkın yayılım alanları ve su yükseklikleri farklı tekerrürlerde incelenerek, ırmak üzerinde yer alan köprüler ve yapılaşmanın olduğu bölgeler için önerilerde bulunulmuştur.

Drought has been experienced frequently in Turkey in the last two decades as the effects of withdrawn water resources become more evident. Drought; causes problems for planners and managers. For this reason, in order to provide an accurate framework for sustainable water resources management, it is necessary to investigate the characteristics of drought events and to estimate the return periods of the drought with the help the regional frequency analysis. In this study, firstly, the characteristics of meteorological droughts in the Kızılırmak Basin which has semi-arid climate characteristics, were determined by using the Standardized Precipitation Index (SPI), Z-Score Index (ZSI), China-Z Index (CZI) and Modified China-Z Index (MCZI) as a measure of drought severity and also the applicability and performance of the selected indices to the basin were investigated. Secondly, regional frequency analysis was performed by using L-moment methods for the maximum drought severity values obtained for each year as a result of the application of the four drought indices on a 12-month time scale. According to the results of the meteorological drought analysis of the four drought indices, it was noted that the most severe and long-lasting droughts occurred mainly in the 2000s, the drought severity values increased as the return period increased and also the drought severity values obtained by MCZI method give the extraordinary results than other indices. Thus, it has been seen that the MCZI method is generally not suitable for use in the basin. According to the results of regional frequency analysis, Kızılırmak Basin, which is H1

Taşkın nehir yatağında bulunan su miktarının çeşitli sebeplerle kesitine sığamayarak, çevresindeki arazilere, yerleşim yerlerine ve canlılara zarar verdiği doğal bir afettir. Yaşamımızı etkileyen taşkınların maddi ve manevi zararlarını azaltmak için taşkını afet haline getiren mekanizmasının iyi bilinmesi ve taşkın meydana gelmeden önce önlemlerin alınması gerekmektedir. Taşkınlar meydana gelmeden önce taşkınlara eğilimli alanların ve bu alanlara ait ekonomik kayıpların belirlenmesi taşkın yönetim planları için önemlidir. Taşkın yönetiminde en önemli adımlardan biri taşkın modellemesidir. Bu modellemede taşkının yayılım alanı, su yüksekliği, su hızı ve ekonomik kayıpları belirlenebilir. Bu çalışmada, Orta Karadeniz Bölgesi’nde yer alan Mert Irmağı Havzası’nın 2 boyutlu taşkın modellemesi yapılmıştır. Ülkemizde, taşkınlar sonucunda oluşabilecek ekonomik kayıpları belirlemede kullanılan, hasar fonksiyonları bulunmamaktadır. Bu çalışma ile birçok Avrupa ülkesinde kullanılan yöntemler kullanılarak tahmini ekonomik kayıpları elde edilmiştir. Taşkın modellemesinde 50, 100, 500 ve 1000 yıllık tekerrür süresine sahip debiler kullanılmıştır. Bu debiler HEC-RAS programıyla modellenmiştir. Modellemeler sonucunda ilgili taşkın tekerrür debilerinin çalışma alanında 4 ile 6 km² alanda yayıldığı, 150-500 milyon TL tahmini hasar verebileceği ve 9 ile 12 bin insanı etkilenebileceği tespit edilmiştir. Bu tahminler bakanlık raporları ile karşılaştırılarak kullanılan hasar fonksiyonlarının ülkemize uygunluğu araştırılmıştır.

Geographic Information Systems (GIS) is a special type of information system in which the data source is a database of spatially distributed features to collect, store, analyze and display geographic data. GIS has gradually come to be recognized as a powerful tool for watershed management due to its ability to link physical, social and economic data. The water industry is one of the fastest growing industries worldwide and its control is only possible with the use of an information system. This study is prepared to give information about the brief history of GIS and the use of GIS in water resources engineering., Coğrafi Bilgi Sistemleri (CBS), coğrafi verileri toplamak, depolamak, analiz etmek ve görüntülemek için mekânsal olarak dağıtılmış özelliklerin veri tabanı niteliğindeki özel bir bilgi sistemi türüdür. CBS, fiziksel, sosyal ve ekonomik verileri birbirine bağlama yeteneği nedeniyle su kaynakları yönetimi ve havza yönetimi için güçlü bir araç olarak kabul edilmektedir. Su endüstrisi dünya çapında oldukça hızlı büyüyen endüstrilerdendir ve kontrolü ancak bir bilgi sisteminin kullanımı ile mümkün olabilmektedir. Bu çalışmanın amacı CBS’nin kısa tarihi hakkında bilgiler vermek ve su mühendisliğinde CBS’nin nasıl kullanıldığını çeşitli örneklerle göstermektir.

Bu çalışma, Samsun Atakum İlçesi Atatürk Bulvarı üzerinde yer alan 9 kavşağın yoğunluk durumlarını değerlendirmek ve kavşaklardaki trafik akışının detaylı bir şekilde analiz edilmesi amacıyla yapılmıştır. Bu amaçla, kavşaklar üzerinden geçen araç cinslerine göre sayımlar yapılmıştır.. Kavşaklarda farklı yönlerdeki trafik akımları için ayrı ayrı sayım verileri toplanmıştır. Bu veriler 15 dakikalık periyotlarla 07.00-19.00 saatleri arasında kaydedilmiştir. Çalışma, belirli bir kavşakta gerçekleşen trafiği analiz ederek, farklı trafik akımlarının (sola dönenler, sağa dönenler ve düz gidenler) her 15 dakikalık periyotta ne kadar yoğun olduğunu belirlemiştir. Bazı kavşaklarda kamera kullanılarak sayımlar yapılırken, bazı kavşaklarda ise saha çalışmalarıyla sayımlar gerçekleştirilmiştir. Yapılan sayımlar sonucunda elde edilen veriler, Coğrafi Bilgi Sistemleri yazılımı olan ArcGIS ortamında Line Yoğunluk Analiz yöntemi kullanılarak yoğunluk haritalarına dönüştürülmüştür. Elde edilen haritalar, kavşakların yoğunluk durumlarının görselleştirilmesine olanak sağlamıştır. Çalışmadaki veriler, CBS yardımıyla araç sayılarına ait tematik haritaların üretilmesinde kullanılmıştır. Sonuçlar incelendiğinde, Ömürevleri Kavşağı'nın en fazla yoğunluğa sahip olduğu, Pelitköy Kavşağı'nın ise en az yoğunluğa sahip olduğu tespit edilmiştir. Bu çalışma trafik akışının düzenlenmesi ve trafik sıkışıklığının düzenlenmesi gibi konularda uygulanabilir çözümler sunabilir.

The aim of this study is to estimate the elevation points used in the creation of the digital elevation model, which is the most important data of the projects and required in the engineering project, using horizontal and vertical location informations and three different heuristic regression techniques. As the study area, an area with mid-level elevations, located in the Marmara region, and covering a part of the intersection of Edirne, Kırklareli and Tekirdağ provinces was chosen. In the study, the estimations were investigated for three different sized areas, and these areas are square areas with the dimensions of 1x1 km, 10x10 km and 100x100 km, respectively. A total of 3500 elevation points were used in the study, and this number is constant in all areas, and 60% of these points were used in the testing phase and 40% in the training phase. The models used in the study are M5 model tree (M5-tree), multivariate adaptive regression curves (MARS) and Least Square Support Vector Regression (LSSVR). The results of the models were evaluated according to three different comparison criteria. These, coefficient of determination (R2), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) were used. When the modeling results are examined; M5-Tree regression method gave the best results (1), LSSVR method was better than MARS methods (2), The most successful input data was found in datasets using X and Y coordinates information, and the worst results were found in datasets using X coordinates (3). As the study area increased, the model performance did not improve (4). The least error was obtained in the modeling of 1x1 km area, and the highest R² was obtained from the modeling of 10x10 km area (5). It was concluded that the M5-tree method is a very successful method in height modeling.

© 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.In this study, monthly solar radiation (SR) estimation was performed using five different machine learning-based approaches. The models used are support vector machine regression (SVMR), long short-term memory (LSTM), Gaussian process regression (GPR), extreme learning machines (ELM) and K-nearest neighbors (KNN). Modeling of these approaches was carried out in two stages. In the first stage, VIF analysis was carried out to develop the model. Thus, the input parameters that decrease the performance of the model are removed. In the second stage, remaining input parameters such as meteorological data, station location data and spatial and temporal information were used in the forecasting modeling according to the correlation SR. In this study, the data set is divided into two parts as test and training. 30% was used in the testing phase, and 70% of the data was used in the training phase. When comparing models, the following error statistics were used: Nash–Sutcliffe efficiency coefficient (NSE), mean absolute error (MAE), mean absolute relative error (MARE), root-mean-square error (RMSE) and coefficient of determination (R2). In addition, Taylor diagrams, violin plots, box error, spider plot and Kruskal–Wallis (KW) and ANOVA test were utilized to determine robustness of model's forecast. As a result of the study, the KW test and ANOVA test results showed that the data of many models were from the same population with observations, and it has proved that LSTM and GPR algorithms are applicable, valid and an alternative for SR forecasting in Turkey, which has arid and semi-arid climatic regions.

Image processing technique has been used frequently in the solution of engineering problems recently. In engineering studies, photographs are taken at certain intervals between the initial state of the material and the state after the change, and changes during the study are observed with the Image processing technique. Based on these photos, the change is transferred to numerical data and the change of the material is observed thanks to these data. Package program systems are used in Image processing technique applications. But these systems are quite expensive systems. In this study, a simpler and feasible system has been developed. The initial sliding test was carried out on 9 single-layer wall systems with natural stones in 20 * 30 * 10 cm dimensions. The displacement values formed on the walls under load during the experiments were measured with the help of potentiometric linear rulers. At the same time, photographs were taken at certain intervals from the baseline to the conclusion of the experiment. The photographs were digitized in the ArcGIS program and the changes on the wall were converted into numerical data. Experimental data and data obtained by photographs were compared. As a result of this comparison, 84% similarity is observed between experimental values and analytical values. It is observed that the image digitization application performed as a result of the study yielded very successful results. In this context, it is believed that the use of this system will be both fast and economically beneficial in larger scale experiments and the number of data.

Teknoloji gelişme bölgelerinin yer seçimi, son yıllarda dünyada ve ülkemizde önemli konular arasında yer almaktadır. Teknoloji gelişme bölgelerinin tüm paydaşlara yakın olması bilgi ve teknolojinin transferini kolaylaştırmakta ve etkin iş birliklerinin ortaya çıkmasını sağlamaktadır. Bu nedenle bu bölgelerden en verimli şekilde yararlanabilmek için yer seçiminde en uygun alanların belirlenmesi oldukça önemli bir araştırma konusudur. Bu çalışmada Konya il merkezinde teknoloji geliştirme bölgesi için uygun alanlar Coğrafi Bilgi Sistemleri (CBS) ve Çok Ölçütlü Karar Analizi (ÇÖKA) yöntemlerinden Analitik Hiyerarşi Yöntemi (AHY) kullanılarak belirlenmiştir. Analizde kullanılan kriterler ve kısıtlar; teknolojik, ekonomik ve çevresel açıdan değerlendirme yapılarak belirlenmiştir. Veriler CBS ortamına aktarılmış, kriter ağırlıkları belirlenmiş ve ikili karşılaştırma yöntemi kullanılarak teknoloji gelişme bölgelerine ait harita oluşturulmuştur. Çalışmanın sonucunda, Konya il merkezinde toplam 8 adet ekilebilir arazi (422.763,126 m²), 8 adet Mera (157.845,420 m²), 23 adet mevcut kullanımı olmayan arazi (178.978,380 m²) ve 21 adet süreksiz yoğunluklu kentsel kumaş alanı (245.766,322 m²) Teknoloji Geliştirme Bölgesinin kurulabileceği uygun alanlar olarak belirlenmiştir. Böylece CBS tabanlı AHY’nin Teknoloji Geliştirme Bölgesinin kurulabileceği yer seçimi problemlerinde etkin bir şekilde kullanılabileceğini ortaya konmuştur.

Solar radyasyon (SR), enerji dönüşümü, meteoroloji, tarım ve hayvancılık ile ilgili çalışmalar için önemli bir parametredir. Solar radyasyonun belirlenebilmesi için gereken alıcıların tüm noktalar için temin edilememesinden dolayı bu parametreyi çeşitli yöntemlerle tahmin edilebilir. Bu çalışmada, Türkiye’nin aylık ortalama solar radyasyon değerleri (kWsa/m²) M5 model ağacı (M5-tree), sezgisel regresyon tekniği kullanılarak MATLAB platformunda tahmin edilmiştir. Solar radyasyon komşu ölçüm istasyonlarına ait konum bilgileri ve periyodiklik bileşeni olan ay değerleri kullanılarak tahmin edilmiştir. Modellerde kullanılan 2004-2018 yıllarını barındıran solar radyasyon verileri Meteoroloji Genel Müdürlüğü’nden temin edilen ve uydu tabanlı hibrit bir model olan HELIOSAT model verileridir. Çalışmada Türkiye’nin 81 iline ait ölçüm istasyonları kullanılmıştır. 81 istasyondan alınan 12 aylık verilerin %75’i eğitim sürecinde kullanılırken %25’i test sürecinde kullanılmıştır. Rastgele seçilen test istasyonlarından elde edilen solar radyasyon tahminleri gözlenen verilerle karşılaştırılmıştır. Bu karşılaştırmalarda kullanılan yöntemler, Karekök Ortalama Karesel Hata (KOKH), Ortalama Mutlak Hata (OMH), Ortalama Mutlak Bağıl Hata (OMBH) ve Determinasyon Katsayısı (R2) yöntemleridir. Sonuç olarak solar radyasyonun tahmin edilmesinde makine öğrenmesi tekniği kullanılarak elde edilen tahminler 4 farklı hata kriteri yöntemiyle karşılaştırılmıştır. Bu sayede makine öğrenmesi algoritmalarının literatürde yer alan kabul görmüş yöntemlere göre daha kolay uygulanabileceği ve alternatif bir yöntem olabileceği ispatlanmaya çalışılmıştır.

North Atlantic Oscillation (NAO) occurring on the North Atlantic Ocean affects hydrologic parameters such as temperature and precipitation in the region where temperature differences occurring on the ocean surface are transported by ocean currents. These effects and the increasing demand for water resources in recent years have made it necessary to use the limited water resources in nature in the most appropriate way to meet the needs. In order to examine the change of water resources, homogeneity and trend analyzes are carried out. In this study, Spearman Rho test and Modified Mann-Kendall test, which takes into account autocorrelation, were applied for standardized NAO data published by the National Weather Service Climate Prediction Center and 12-month precipitation data (between 1961-2015) of 18 stations in the Black Sea region. The study was examined in 95% of the confidence interval. The SRho correlation test was applied to determine the relationship between NAO index and Black Sea Region precipitation. When the study results are analyzed, a weak negative trend was detected between the data of North Atlantic Oscillation and the precipitation data of 18 stations analyzed in the Black Sea Region.

Determining the Manning roughness coefficients is one of the most important steps in flood modeling. The roughness coefficients cause differences in flood areas, water levels, and velocities in the process of modeling. This study aims to determine both the Manning roughness coefficient in the river sections and outside of the river regions by using the Cowan method and remote sensing technique in the flood modeling. In the flood modeling, FLO-2D Pro program which can simulate flood propagation in two dimensions was utilized. Mert River in Samsun province located in the northern part of Turkey was chosen as the study area. Samples taken from the river were subjected to sieve analysis, the types of constituent material were determined according to the median diameters and the roughness coefficients were obtained using the Cowan method. For regions outside of the river were applied the maximum likelihood method being one of the controlled classification methods. Manning roughness values were assigned the classified image sections. Remote sensing techniques were meticulously employed to achieve time management in areas outside the river and a new approach was proposed in the Manning assessment of flood areas to ensure uniformity in the study area. In the classification made using the maximum likelihood method, the overall classification accuracy was 92.9% and the kappa ratio “κ” was 90.64%. The results were calibrated with the last hazardous flood images in 2012 and HEC-RAS 2D program, another flood modeling program.

Determining water level changes in lakes is significant for sustainable water supply planning, flood control, water resource management, and economic development with ecosystem sustainability. Trend analysis is a commonly used tool for detecting changes in hydrologic time series of quantities such as lake level, precipitation and temperature. Trend analysis of meteorological variables is very important for assessing the long-term changes in lake levels. This study examines long-term changes in the lakes in the Central Anatolia region of Turkey. Kizoren and Timras sinkhole lakes and Lakes Tuz and Beysehir are some of the lakes located in this region. Changes in these lakes were examined along with changes in precipitation trends and human effects. The precipitation stations representing these lakes were determined using Thiessen polygons. In this study, the well-known nonparametric Mann–Kendall trend test (MK) was used. Before performing trend analysis, homogeneity and autocorrelation tests were applied for the time series. The standard normal homogeneity test (SNHT) was used to evaluate homogeneity. Trend analysis was performed before and after the change points the inhomogeneous series for the period of the homogeneous series. In addition, the modified Mann–Kendall method (MMK) was performed to examine the strong autocorrelations seen in the entire time series. The results showed that precipitation station data are homogeneous and lake levels are inhomogeneous. Trend analysis was performed by determining the change points for the inhomogeneous lakes. There were no significant trends after the change points for the Lake Tuz and Timras sinkhole lake. These results are supported by the insignificant trends in precipitation stations. Contrary to the trend analysis results, the increase in the water level of Beysehir Lake was determined to increment from the water transferred to the lake. The decrease in the water level of Kizoren sinkhole was a result of anthropogenic effect rather than precipitation. The results confirm that lake levels have been affected by precipitation trends and anthropogenic effects.

Akarsu akımlarının tahmini havza ve taşkın yönetiminde oldukça önemlidir. Doğrusallık göstermeyen ve birçok parametreye bağlı olan hidrolojik olayların tahminleri oldukça zor bir konudur. Bu çalışmada Mersin Lamas Nehri aylık ortalama akım verileri kullanılarak Yapay Sinir Ağları (YSA), Destek Vektör Makineleri (DVM) ve Derin Öğrenme (DÖ) optimizasyonları kullanılarak akım modellemesi yapılmış ve karşılaştırılmıştır. Sonuçlar incelendiğinde problemin çözümünde en iyi yöntemin DÖ olduğu YSA ve DVM yöntemlerinin ise buna göre daha başarısız sonuçlar verdiği gözlemlenmiştir. En iyi sonucu DÖ’ye ait 1 giriş modeli vermiştir (KOKH= 0.7647, OMH=0.7370, NSE=0.9724, OMBH=27.9326, R²=0.9962)., Estimation of stream flows is very important in basin and flood management. It is a very difficult topic to predict hydrological events that do not show linearity and depend on many parameters. In this study, streamflow modeling has been made and compared by using Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Deep Learning (DL) optimizations using monthly average streamflow data of Mersin Lamas River. When the results are examined, it has been observed that the best method in solving the problem is ANN and SVM methods, which give more unsuccessful results. Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Nash Sutcliffe model Efficiency coefficient (NSE), Mean Absolute Relative Error (MARE) and Coefficient of Determination (R2) were used as comparison criteria. 1 input model of DL gave the best result (RMSE= 0.7647, MAE=0.7370, NSE=0.9724, MARE=27.9326, R²=0.9962).

Nehir yataginda veya taskin alanlarinda ortalama akim hizini bulmak icin gunumuzde siklikla kullanilan Manning formulu Robert MANNING tarafindan 1889 yilinda yayinlanmistir. Formulde yer alan n puruzluluk katsayisinin belirlenmesinde puruzlulugu olusturan parametrelerin cogunu icerisinde barindiran Cowan yontemi ve ayrica uzaktan algilama teknikleriyle gerek nehir kesitinde gerekse kesit disindaki alanlarda belirlenmesi calismanin amacini olusturmaktadir. Calisma alani olarak Samsun Mert Irmagi ve kensel sinirlar icerisinde kalan bolge secilmistir Irmak uzerinden alinan numuneler elek analizine tabi tutulmus, ardindan medyan caplarina gore yatak malzemesinin cinsi belirlenerek puruzluluk degerleri Cowan yontemi kullanilarak elde edilmistir. Irmak disinda yer alan bolgeler icin ise kontrollu siniflanrdirma yontemlerinden En Yuksek Olasilik Yontemi (maksimum likelihood) yontemi kullanilarak siniflandirilan goruntu bolumlerine n degerleri atanmistir. Uzaktan algilama tekniklerinin kullanilmasiyla zamandan tasarruf edilmis, Manning degerlendirmesinde yeni bir yaklasim onerilerek calisma alaninda yeknesaklik saglanmistir. En Yuksek Olasilik Yontemi kullanilarak yapilan siniflandirmada, genel siniflandirma dogrulugu (yuzde olarak) 92.9, kappa orani “κ” ise; % 90.64’dur. Bulunan “κ” degeri 0.75’den buyuk olmasi durumunda siniflandirma performansinin iyi oldugunu gostermektedir. Boylece gerek taskin modellemelerinde gerekse su yapilarinin tasariminda kullanilacak Manning degerleri elde edilmistir.

Taban akımı yağış miktarı azaldığında veya kurak zamanlarda yağış olmadığında nehrin kurumasını engeller ve nehirdeki akışın devamlılığını sağlar. Ayrıca taban (baz) akımı çok yağış alan ve kar erimesinin uzun süre devam ettiği drenaj alanlarında taşkınları etkilemektedir. Bu nedenle özellikle taşkın ve kuraklık çalışmalarında baz akımı dikkate alınmalıdır. Bu çalışmada baz akımı, debi-sürek eğrisi kullanılarak Samsun-Kürtün Irmağı için elde edilmiştir. Kürtün Irmağı için incelenen bu çalışmada zamanın %95’inde var olan debi taban akımı olarak alınmıştır. Kürtün Irmağı, Yeşilırmak-Kızılırmak nehirlerinin Karadeniz’e çıkış yaptığı ara bölgede yer almaktadır. Nehir içme suyu ve sulama suyu açısından Karadeniz’in en gelişmiş kenti olan Samsun için oldukça önemlidir. Ayrıca nehirde birçok kez taşkın meydana gelmiştir ve bu taşkınlarda birçok alan sular altında kalmıştır. Çalışmada taban akımını belirlemek için ırmak üzerinde yer alan 1964-2020 yılları arası ortalama akım verilerinin gözlemlendiği D14A014 numaralı istasyon kullanılmıştır. Çizilen debi-süreklilik eğrisinden baz akım 4.47 m3/s olarak tespit edilmiş ve yıllara göre taban akımının eğilimi Mann-Kendall trend analizi ile incelenmiştir., The base-flow prevents the river from drying out when the amount of precipitation decreases or there is no precipitation in dry times and ensures the continuity of the flow in the river. Also, the base-flow affects floods in drainage areas where there is a lot of precipitation and snow melting continues for a long time. Therefore, the base flow should be taken into consideration in flood and drought studies. In this study, the base-flow was obtained for the Samsun-Kürtün river located in the central part of the Black Sea Region by using the flow continuity curve. In this study analyzed for Kürtün river, the flow that exists in 95% of the time is taken as the base flow. The most developed city of the Black Sea in terms of water resources and urbanization, the river is very important for Samsun. In addition, many floods have occurred in the river, and many areas have been flooded in these floods. The station D14A014 whose average stream datas on the river between 1964-2020 were observated was used to determinate the base flow in this study. The base flow was measured as 4.47 m3/s by flow continuity curve drawn and the trend of the base flow was examined by years with the Mann-Kendall trend analysis.

Doğu Karadeniz Bölgesi Türkiye’nin en çok yağış alan bölgesidir. Bu nedenle sıcaklık, yağış gibi meteorolojik parametrelerdeki değişimler bölgedeki hayatı büyük ölçüde etkilemektedir. Bu değişimlerin aşırı yağışların etkisi ile taşkın veya giderek artan sıcaklıkların etkisiyle ortaya çıkan kuraklık gibi insan hayatını ve bölgedeki üretimi etkileyerek sıcaklık ve yağış değişkenleri üzerinde meydana gelen değişikliklerin önemini gözler önüne sermektedir. Bu değişiklikleri tespit etmek amacıyla birçok parametrik ve parametrik olmayan yöntem geliştirilmiştir, bu yöntemlerin başında trend analizleri gelmektedir. Bu çalışmada Meteoroloji Genel Müdürlüğü’ne ait Giresun, Trabzon, Rize, Artvin, Gümüşhane, Bayburt illerinde bulunan 6 istasyondan elde edilen 1960-2016 yılları arasını kapsayan 57 yıllık sıcaklık, buharlaşma, rüzgar ve yağış verileri üzerinde %95 güven aralığında gerçekleştirilen trend analizlerinde parametrik bir yöntem olan Lineer Trend yöntemi ve parametrik olmayan Modifiye Mann-Kendall yöntemi kullanılmıştır. Bu çalışmanın sonucunda Lineer Trend yöntemi ve Modifiye Mann-Kendall yönteminden elde edilen sonuçların %86.1-97.22 oranında uyuştuğu saptanmıştır. Sonuçlar olarak incelendiğinde sıcaklık ve yağışların artan yönde eğilim gösterdiği, rüzgarların Trabzon istasyonunda artan, Rize ve Bayburt istasyonlarında azalan yönde anlamlı eğilimler gösterdiği tespit edilmiştir. Buharlaşma verileri ise Bayburt istasyonunda azalan, Gümüşhane ve Trabzon istasyonlarında artan bir eğilim göstermektedir., The Eastern Black Sea region is the rainiest region of Turkey. Therefore, changes in temperature, precipitation, etc., affect life in the region to a great extent. These changes show the importance of changes in temperature and precipitation variables by affecting human life and production in the region, such as flood caused by excessive precipitation or droughts caused by global warming. In order to detect these changes, many parametric and non-parametric methods have been developed. In this study trend analyzes were applied on 57 (1960-2016) years temperature, winds, evaporation and precipitation data of 6 stations in Giresun, Trabzon, Rize, Artvin, Gümüşhane, Bayburt provinces belonging to Turkish State Meteorological Service. Study was performed in 95% confidence interval. As a result of this study, the results obtained from Linear Trend method and Modified Mann-Kendall method were found to be 86.1-97.22 % concordant. When the results of the Eastern Black Sea Region are examined, it is observed that temperature and precipitation are showed in increasing direction, winds showed significant tendencies in the direction of increasing in Trabzon station and decreasing in Rize and Bayburt stations. Evaporation data showed decreasing trend in Bayburt station and increasing trend in Gümüşhane and Trabzon stations.

The digital elevation model (DEM) is the name given to a digital structure used to indicate the surface. Determination of features such as elevation, basin slope and basin area are very important in engineering applications. These properties are determined by the DEM and their power to represent accuracy or truth is vital in engineering applications. In addition to the latitude (X), longitude(Y) coordinate information, altitude information is required, and intermediate values are determined by different methods for DEM. In this study, Mert River Basin Samsun (Turkey) was chosen as the application area. Heights are estimated from X, Y coordinate information. Three different Artificial Neural Networks, IDW and Kriging methods were used. Artificial Neural Networks (ANN) were analyzed with three different inputs. These are: (i) x coordinate information; (ii) y coordinate information; (iii) It is in the form of x and y coordinate information and are used Radial Based Artificial Neural Network, Multilayer Artificial Neural Network and Generalized Artificial Neural Network. X and Y coordinate information was used in IDW and Kriging interpolation methods. Results were evaluated using Coefficient of Determination (R²), Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) as comparison criteria. According to the modeling results: It was observed that the results of all methods reached a sufficient level of accuracy. Kriging method was found to be the most successful model, followed by IDW and ANN.

Trend analyses of meteorological variables play an important role in assessing the long-term changes in water levels for sustainable management of shallow lakes that are extremely vulnerable to climatic variations. Lake Mogan and Lake Eymir are shallow lakes offering aesthetic, recreational, and ecological resources. Trend analyses of monthly water levels and meteorological variables affecting lake levels were done by the Mann-Kendall (MK), Modified Mann-Kendall (MMK), Sen Trend (ST), and Linear trend (LT) methods. Trend analyses of monthly lake levels for both lakes revealed an increasing trend with the Mann-Kendall, Linear, and Sen Trend tests. The Modified Mann-Kendall test results were statistically significant with an increasing trend for Eymir lake levels, but they were insignificant for Mogan lake due to the presence of autocorrelation. While trend analyses of meteorological variables by Sen Test were significant at all tested variables and confidence levels, Mann-Kendall, Modified Mann-Kendall, and Linear trend provided significant trends for only humidity and wind speed. The trend analyses of Sen Test gave increasing trends for temperature, wind speed, cloud cover, and precipitation, and decreasing trends for humidity, sunshine duration, and pan evaporation. These results show that increasing precipitation and decreasing pan evaporation resulted in increasing lake levels. The results further demonstrated an inverse relationship between the trends of air temperature and pan evaporation, pointing to an apparent &ldquo, Evaporation Paradox&rdquo, also observed in other locations. However, the increased cloud cover happens to offset the effects of increased temperature and decreased humidity on pan evaporation. Thus, all relevant factors affecting pan evaporation should be considered to explain seemingly paradoxical observations.

Soft computing models are known as an efficient tool for modelling temporal and spatial variation of surface water quality variables and particularly in rivers. These model’s performance relies on how effective their simulation processes are accomplished. Fuzzy logic approach is one of the authoritative intelligent model in solving complex problems that deal with uncertainty and vagueness data. River water quality nature is involved with high stochasticity and redundancy due to the its correlation with several hydrological and environmental aspects. Yet, the fuzzy logic theory can give robust solution for modelling river water quality problem. In addition, this approach likewise can be coordinated with an expert system framework for giving reliable and trustful information for decision makers in enhancing river system sustainability and factual strategies. In this research, different hybrid intelligence models based on adaptive neuro-fuzzy inference system (ANFIS) integrated with fuzzy c-means data clustering (FCM), grid partition (GP) and subtractive clustering (SC) models are used in modelling river water quality index (WQI). Monthly measurement records belong to Selangor River located in Malaysia were selected to build the predictive models. The modelling process was included several water quality terms counting physical, chemical and biological variables whereas WQI was the target variable. At the first stage of the research, statistical analysis for each water quality parameter was analyzed toward the WQI. Whereas in the second stage, the predictive models were established. The finding of the current research provides an authorized soft computing model to determine WQI that can be used instead of the conventional procedure that consumes time, cost, efforts and sometimes computation errors.

This study investigates the applicability of three different soft computing methods, least square support vector regression (LSSVR), multivariate adaptive regression splines (MARS), and M5 Model Tree (M5-Tree), in forecasting SO2 concentration. These models were applied to monthly data obtained from Janakpuri, Nizamuddin, and Shahzadabad, located in Delhi, India. The models were compared with each other using the cross validation method with respect to root mean square error, mean absolute error, and correlation coefficient. According to the comparison, LSSVR provided better accuracy than the other models, while the MARS model was found to be the second best model in forecasting monthly SO2 concentration. Results indicated that the applied models gave better forecasting accuracy in Janakpuri station than the other stations. The results were also compared with previous studies and satisfactory results were obtained from three methods in modeling SO2 concentrations.

Određivanje Manningovih koeficijenata hrapavosti jedan je od najvažnijih koraka u modeliranju poplava. Koeficijenti hrapavosti u procesu modeliranja uzrokuju razlike u poplavnim područjima, razinama vode i brzinama. Cilj ove studije je utvrditi koeficijent hrapavosti u riječnim odsječcima te i izvan riječnih područja korištenjem Cowanove metode i tehnike daljinskog istraživanja. U modeliranju poplave korišten je program FLO-2D Pro koji može simulirati širenje poplave u dvije dimenzije. Za područje istraživanja odabrana je rijeka Mert u provinciji Samsun koja se nalazi u sjevernom dijelu Turske. Uzorci uzeti iz rijeke analizirani su pomoću sita, gdje su vrste sastavnog materijala određene prema srednjim promjerima, a koeficijenti hrapavosti dobiveni su Cowanovom metodom. Za područja izvan rijeke primijenjena je metoda najveće vjerojatnosti koja je jedna od kontroliranih metoda klasifikacije. Manningove vrijednosti hrapavosti dodijeljene su klasificiranim odsječcima slike. Tehnike daljinskih mjerenja pomno su korištene kako bi se procijenili koeficijenti hrapavosti u područjima izvan rijeke, a novi je pristup predložen u Manningovoj procjeni poplavnih područja kako bi se osigurala ujednačenost na istraživanom području. U klasifikaciji izvedenoj metodom najveće vjerojatnosti, ukupna točnost klasifikacije bila je 92,9%, a kappa omjer “κ” 90,64%. Rezultati su kalibrirani prema posljednjim slikama opasnih poplava 2012. godine i HEC-RAS 2D programom, koji je također program za modeliranje poplave., Determining the Manning roughness coefficients is one of the most important steps in flood modeling. The roughness coefficients cause differences in flood areas, water levels, and velocities in the process of modeling. This study aims to determine both the Manning roughness coefficient in the river sections and outside of the river regions by using the Cowan method and remote sensing technique in the flood modeling. In the flood modeling, FLO-2D Pro program which can simulate flood propagation in two dimensions was utilized. Mert River in Samsun province located in the northern part of Turkey was chosen as the study area. Samples taken from the river were subjected to sieve analysis, the types of constituent material were determined according to the median diameters and the roughness coefficients were obtained using the Cowan method. For regions outside of the river were applied the maximum likelihood method being one of the controlled classification methods. Manning roughness values were assigned the classified image sections. Remote sensing techniques were meticulously employed to achieve time management in areas outside the river and a new approach was proposed in the Manning assessment of flood areas to ensure uniformity in the study area. In the classification made using the maximum likelihood method, the overall classification accuracy was 92.9% and the kappa ratio “κ” was 90.64%. The results were calibrated with the last hazardous flood images in 2012 and HEC-RAS 2D program, another flood modeling program.

Streamflow forecasting and predicting are significant concern for several applications of water resources and management including flood management, determination of river water potentials, environmental flow analysis, and agriculture and hydro-power generation. Forecasting and predicting of monthly streamflows are investigated by using three heuristic regression techniques, least square support vector regression (LSSVR), multivariate adaptive regression splines (MARS) and M5 Model Tree (M5-Tree). Data from four different stations, Besiri and Malabadi located in Turkey, Hit and Baghdad located in Iraq, are used in the analysis. Cross validation method is employed in the applications. In the first stage of the study, the heuristic regression models are compared with each other and multiple linear regression (MLR) in forecasting one month ahead streamflow of each station, individually. In the second stage, the models are evaluated and compared in predicting streamflow of one station using data of nearby station. The research investigated also the influence of the periodicity component (month number of the year) as an external sub-set in modeling long-term streamflow. In both stages, the comparison results indicate that the LSSVR model generally performs superior to the MARS, M5-Tree and MLR models. In addition, it is seen that adding periodicity as input to the models significantly increase their accuracy in forecasting and predicting monthly streamflows in both stages of the study.

Climate change impacts are perceived today as being very noticeable; determination of precipitation, wind, flow, evaporation and temperature trends has become essential for water resources and engineering projects in management and planning. Nowadays there is a lot of studies have been made in progress on global and regional climate changes in literature. In this study, monthly, annual and seasonal trends in average temperature, total precipitation, and average wind speed data calculated by Mann-Kendall, linear trend and Sen’s trend tests. The gauge station is located in Samsun, which is the largest city of the Turkey’s Black Sea region. The data sets are obtained for the period 1980 to 2015. According to the results, at the levels of significance of 5% and 10%, Mann-Kendall's statistical results were found to be generally similar to those of trend analysis methods.

This paper investigates the accuracy of three different adaptive neuro-fuzzy inference systems (ANFISs), ANFIS with grid partition (ANFIS-GP), ANFIS with substructive clustering (ANFIS-SC),...

Variations in temperature and precipitation have direct impacts on the physical, chemical and biological characteristics of the shallow lakes. This paper examines the possible linkages between climate variables and the water levels of shallow interconnected Lakes Mogan and Eymir, located 20 km south of Ankara in Central Anatolia. The variations in the lakes’ water levels during 1996–2015 are studied and the impacts of climate variables on the lake levels are assessed to address the long-term consequences. The nonparametric Mann–Kendall test was used to quantify trends in the climate variables and the lakes’ level fluctuations between the observation periods 1998–2007 and 2008–2014. Statistical analyses results showed that precipitation and temperature have crucial influence on the variations in the lakes’ levels. The projected increase in temperature and decrease in precipitation over the next century may produce substantial decreases in lake levels, with consequent drying of both lakes.

Bu araştırmada, Karadeniz’e dökülenakarsulardaki 52 adet akım gözlem istasyonuna ait Yıllık Anlık Maksimum Akım(YAMA) verileri kullanılarak L-Momentler yöntemiyle bölgesel frekans analiziyapılmıştır. Hosking tarafından sağlanan bilgisayar programı kullanılarakgerçekleştirilen uygulama beş büyük havzayı kapsayan çalışma alanının tekbaşına bir bütün olarak homojen bir bölge olmadığını ortaya çıkarmıştır (H1=13,60>>1,0).Bu havzaların fizyografik ve hidrolojik özellikleri göz önüne alınarak ve H1kriterine göre, çalışma alanı 11 farklı homojen bölgeye ayrılmıştır. ZDISTuygunluk testi sonuçlarına göre 11 alt bölgenin beşinde Genelleştirilmiş Normaldağılım en uygun olasılık dağılımı olarak belirlenmiştir ve GenelleştirilmişEkstrem Değerler, Pearson Tip 3 ve Genelleştirilmiş Lojistik dağılımlarısırasıyla üç, iki ve bir alt bölgede en uygun dağılım olarak bulunmuştur. Budağılımlarla yapılan indeks‒taşkın frekans analizleri sonucunda, çalışma alanıiçin 2 yıl ile 1000 yıl arası tekerrür periyotlarında grid yöntem ile bölgeselYıllık Anlık Maksimum Akım haritaları oluşturulmuştur., In this study, Regional Frequency Analysis ofannual flood peaks (AFP) series of natural streams discharging to the Black Seausing AFP series recorded 52 stream-gauging stations is done by L-Momentsmethod. Application of method performed by using computer program provided byHosking reveals that working area including five major basins cannot be treatedas a single homogeneous region as a whole (H1=13.6>>1.0).Taking into consideration physiographical and hydrological peculiarities ofthese basins and according to H1 criteria, working area is dividedinto 11 different homogeneous regions. According to ZDISTgoodness-of-fit criterion, Generalized Normal Distribution is determined to bethe best probability distribution in five of eleven sub-regions and GeneralizedExtreme Values, Pearson Type 3, Generalized Logistic distributions are found tobe the best in three, two, and one sub-regions, respectively. As a result of index-floodfrequency analysis with these distributions, regional annual flood peaks mapscovering range of return periods from 2 to 1000 years are produced by gridmethod for working area.

The ability of three different heuristic regression methods, least-square support vector regression (LSSVR), multivariate adaptive regression spline (MARS), and M5 model tree (M5Tree), was investigated in forecasting hydrological time series. In the study, daily streamflow data from two stations in Turkey were used by employing cross validation method. Models were compared with each other with respect to root-mean-square error, mean absolute error and determination coefficient. The LSSVR was found to be superior to the MARS and M5Tree models in daily streamflow forecasting. The effect of periodicity component and log transformation was also examined in forecasting daily streamflows by adding day number of each streamflow data and log transformated data as inputs to the applied models. It was observed that the accuracy of LSSVR model was slightly increased by adding periodicity component in both stations while the accuracy of M5Tree model was decreased in forecasting streamflows of Derecikviran station. The accuracy of all the three models was not increased by using log transformation of the daily streamflow data as inputs.