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  • Öge
    Hidrolojik model yapısının ve kalibrasyon algoritmasının debi simülasyon performansına etkisi
    (Lisansüstü Eğitim Enstitüsü, 2022-06-22) Alp, Harun ; Demirel, Mehmet Cüneyd ; Aşıkoğlu, Ömer Levend ; 501191507 ; Hidrolik ve Su Kaynakları Mühendisliği
    Dünya genelinde gerek tüketimin artması, gerek küresel ısınma gibi etkenlerden ötürü su kaynaklarının korunması gün geçtikçe önem kazanmaktadır. Bu bağlamda yapılacak her müdahale, alınacak her önlem öncesinde bir hazırlık ve planlama süreci gerektirir. Günümüzde su yönetimi stratejilerine yön veren hidrolojik modeller, farklı yöntemlerle kurdukları yağış-akış ilişkisi ile başta debi olmak üzere hidroloji bilimine katkıda bulunan ve doğal döngüye dahil olan bir çok kavrama dair çıktılar sunmaktadır. Bu çalışmada hidrolojik döngünün devam edebilmesinde en önemli etkenlerden olan DSİ ve EİE kontrolündeki 523 no'lu akım gözlem istasyonuna ait debi değerleri referans alınarak, bu süreçleri üç farklı yapıda işleyen Génie Rural à 4 paramètres Journalier (GR4J), Soil Water Asesessment Tool Plus (SWAT+) ve mesoscale Hydrological Model (mHM) modellerinin ve bu modellere ait parametrelerin kalibrasyonunda kullanılan Levenberg-Marquardt (LM), Shuffled Complex Evolution (SCE) ve Covariance Matrix Adoption Evolution Strategy (CMAES) algoritmalarının performansı Gediz Havzası'nın doğduğu bölge olan Acısu Havzası için değerlendirilmiştir. Hidrolojik modeller kullanım amaçlarına göre farklı parametreler ve farklı mekansal çözünürlükler kullanarak verileri işlemektedir. Temeli bir performans karşılaştırmasına dayanan bu çalışmada modellerin ve algoritmaların birleşiminden çıkacak sonuçların tarafsız bir şekilde değerlendirilebilmesi amacıyla, üç hidrolojik modelde de ortak olan girdiler için (yağış, sıcaklık, potansiyel evapotranspirasyon) European Center for Medium-Range Weather Forecast (ECMWF) ERA5 kaynağından elde edilen veri setleri kullanılmıştır. 30 km x 30 km çözünürlüğe sahip olan ERA5 verisi, toplu model olan GR4J için tek bir zaman serisi olarak tüm havza ölçeğine, yarı dağılı model olan SWAT+ için alt havzaları temsil edecek ölçeğe ağırlıklı ortalama yöntemi ile indirgenmiştir. mHM modeline ise noktasal veri olarak varsayılan ölçeği ile tanımlanmıştır. Sonrasında modellerin ihtiyaç duyduğu diğer girdiler tanımlanmış (DEM, Arazi Kullanımı, Toprak Haritası vb.) ve kurulum aşaması tamamlanmıştır. Çalışmanın devamında, hidrolojik modellerin kalibrasyonu yapılmıştır. Bu aşamada kalibrasyon aracı olarak Parameter Estimation Tool (PEST) kullanılmıştır. Kurulan hidrolojik model dosyalarının PEST ile entegrasyonu gerçekleştirilmiştir ve kalibre edilecek parametrelerin belirlenmesi için otomatik hassasiyet analizi yapılmıştır. GR4J modelinin 4 parametresinden 3'ü, SWAT+ modelinin debi üzerinde etkili olan 20 parametresinden 10'u ve mHM modelinin 66 parametresinden 15'i belirlenen eşik değer üzerinde kalibrasyon aşamasında kullanılmıştır. Kalibrasyon için PEST'in içerdiği 1 lokal (LM), 2 global (SCE ve CMAES) yöntem kullanılırken, kalibrasyon periyodu için 1991-2000 yılları, validasyon periyodu için ise 2002-2005 yılları arasındaki veriler kullanılmıştır. Bu kapsamda üç farklı mekansal çözünürlüğe sahip hidrolojik model yapısının, bir lokal ve iki farklı global yöntem ile kalibre edilerek karşılaştırıldığı ilk çalışma olma özelliği taşımaktadır. Çalışma sonucunda elde edilen bulgular çerçevesinde hidrolojik model yapılarının ve kalibrasyon algoritmalarının debi çıktısı üzerindeki etkisi incelenmiştir. Dokuz farklı model-algoritma kombinasyonuna ait debi çıktıları 7 farklı istatistiksel gösterge ile değerlendirilmiştir (NSE, KSE, R2, RSR, MSE, RMSE). Amaç fonksiyonu olarak Nash-Sutcliffe Efficiency (NSE) değeri ile kalibre edilen kombinasyonlara ait çıktıların, gözlenmiş değerler ile aralarındaki ilişki incelendiğinde, dağılı model olan mHM modeli ile global yöntemlerden CMAES algoritmasının en yüksek debi performansı gösteren entegrasyon olduğu sonucuna ulaşılmıştır. Ulaşılan sonuçların çalışma alanı kapsamında geçerli olduğu ve genelleştirilebilmesi için farklı özelliklere sahip havzalarda da benzer çalışmanın uygulanması gerektiği belirtilmiştir.
  • Öge
    Dam break induced flood analysis by soft computing techniques
    (Lisansüstü Eğitim Enstitüsü, 2022-06-03) Akdemir, Halid ; Bayındır, Cihan ; 501201513 ; Hydraulics and Water Resources Engineering
    Water structures have been one of the basic components that have served the various needs of societies since ancient times. Fresh water resources, which are examples of these structures and have a great impact on the residents in vicinity, are dams. Dams have a variety of purposes such as fresh water supply, waste storage, agricultural irrigation, electricity generation and flood protection. The management of massive structures such as dams is also a critical issue affecting the well-being of societies. The proper management of dams is important not only for the efficient, but also for the safe operation of the water resource. Otherwise, the dams can collapse, as in many examples in the past, causing enormous material and moral damages and losses. The failure of dams can be caused not only by their proper operation, but also by many various reasons such as earthquakes, extreme precipitations, sabotage and aging. Since dams hold large bodies of water, they cause many life losses and material damage in case of their collapse. History has shown this to humanity in a painful way. It is essential that the dams are properly designed and operated in order to prevent them from collapsing. Since these structures are man-made, they can still be demolished, so preparations for this scenario should be done and ready within the scope of risk and disaster management. It is a clear reason that dam operations will become more difficult in rapidly changing climatic conditions. The aging of dams in the coming years is an additional problem to these difficulties. These reasons assurance that dam failure problems will be a hot topic to work on in the years to come. Unfortunately, dam failure problems are inherently complex. The cause of dam failures may not be known because they are very large engineering structures. There can be many different reasons behind a dam collapse. The dam failure process is also difficult to analyze as it involves many variables. Researchers have proposed many different methods and approaches to illuminate this process. As a result of dam failures, a tsunami wave may occur at the downstream, followed by flooding and rising water level over time. In recent years, problems with complex definition and many variables have been analyzed with soft computing methods. Soft computing methods are a very broad field that includes many algorithms and are frequently used in many different branches of science. Their success, especially in the analysis of major nature problems, has been proven by researchers with various studies. Grey relational analysis (GRA), long-short term memory (LSTM) and artificial neural fuzzy inference system (ANFIS), which are used in this study, can be given as example algorithms of soft computing methods. It has been intended to provide a data set to researchers and local governments and aimed to contribute to risk and disaster management in water resources by presenting numerical simulations of some large and aging dams in Turkey and the possible loss of life they will cause. In this context, GRA, LSTM and ANFIS models from soft computing methods were developed and tested for the analysis of dam failure problems and dam break induced flood. In summary, the main purpose of this study is to prove the usability of soft computing techniques in dam break problems and to conduct risk analysis of some aging dams in Turkey within this framework. In the third section titled "Danger level ranking of possible dam failures in Turkey", the 15 aging dams in Turkey which have potential to cause the most dangerous consequences in case of failure were selected intuitively from engineering judgement in order to perform their failure simulation. The selected 15 aging dams are as follows. Seyhan Dam and Hydroelectric Power Plant (HPP), Borçka Dam and HPP, Ürkmez Dam, Eğrekkaya Dam, Tahtalı Dam, Mamasın Dam, Dim Dam and HPP, Kurtboğazı Dam, Atasu Dam, Alibey Dam, Akköprü Dam and HPP, Suat Uğurlu Dam and HPP, Derbent Dam and HPP, Manavgat Dam and HPP, Kirazlıköprü Dam. The failure simulations of the dams were carried out with the worst-case scenario, the sudden collapse failure mode. The simulations performed were made through the HEC-RAS application and the simulation stages were shared step by step. Maximum water depth maps obtained according to the simulation were given for the impact zone of each dam. Using first wave arrival time and affected population values obtained from the simulastions, possible life losses were calculated with the equations of DeKay and Mclelland (1993) and Brown and Graham (1988) and shared for each dam. Accordingly, the collapse of Seyhan Dam and HPP causes the higest life losses among others. The 15 dams were ranked according to the probable loss of life obtained from the DeKay and Mclelland (1993) equation in case of their failure. The GRA model has been developed for the danger level ranking analysis of dams in order to be a more practical solution, since the danger level ranking of dams by numerical analysis is a labor-intensive task that takes a lot of time. The effective attributes of GRA model were chosen as follows. Surrounding population, distance from that population, elevation relative to that population and reservoir size. These selected attributes have been decided from an engineering point of view. The quantitative values of the attributes were determined by engineering evaluation. The output of the model is to rank the dams according to the possible loss of life that may occur in case of failure. According to results produced by the GRA model developed in this frame, the dam that can cause the highest life losses in case of collapse is Adana Dam and HPP. The rankings produced by the GRA model and by the numerical analysis were compared and interpreted. The results indicate that the GRA model and numerical analysis produce similar ordering. Thus, it has been revealed that the GRA method is an effective and useful tool and can produce practical solutions for risk and disaster management studies on water structures. In addition, simulations of failure of 15 aging dams in Turkey and their possible consequences were brought to the literature. The implications of this section will contribute to the risk and disaster management in water structures. In the fourth section titled "Prediction of dam break induced flood parameters by LSTM network and ANFIS", the analysis of the most vital parameters that determine the lethality level of flood disasters caused by dam failure was carried out with LSTM and ANFIS from soft computing methods. Past events don't provide a very adequate data set, as well as experimental studies also remain very fictional. In real situations, every flood event is unique, which has different forces that drive and effect damage level. Therefore, it is necessary to examine such flood events on a case-by-case basis and to carry out special studies for each region. Performing flood simulations of regions with numerical analysis is the most satisfactory method currently available. Data obtained from simulations of sample regions by numerical analysis were selected for feeding and testing LSTM and ANFIS models developed under this title. The main parameters affecting the lethality level of the flood are water depth, flow velocity, wave damping and first wave arrival time. The data set of these parameters was created by simulating the Alibey Dam break and Froehlich (2008) approach from the breach development methods accepted in the literature defined in the HEC-RAS application was chosen as its breach development method. The information about the measurement locations determined in the impact area of Alibey Dam and the data obtained from these points were given in the relevant section. Since flood parameters such as water depth, flow velocity and wave damping are data sets consisting of time and location series, it was preferred to create LSTM models in the analysis of these parameters. On other hand, ANFIS model was found more convenient for the analysis of the first wave arrival time because it was deemed more appropriate to create a multivariate model. Due to the suitability of the data sets of water depth, flow velocity and wave damping, nonlinear autoregressive LSTM models containing the same following structure were created for the analysis of water depth, flow velocity and wave damping parameters, which does not need any external input and takes the previous output as input. The training, validation and testing ratios were chosen as 20%, 5% and 75%, respectively. The training function was selected as bayesian regularization backpropagation and the dataset dividing function was designed as divedeblock so called considering it is prominent to test the last part of the data. In this case, the last 75% of the data set was exposed testing. Coefficient of correlation and root mean squared error values expressing the success of the LSTM models developed for the analysis of water depth in the rezervoir, water depth in downstream, flow velocity and wave damping parameters in the test phase are 0.9988 and 0.5798, 1.0000 and 0.0192, 0.9995 and 0.0018, 0.9986 and 0.1804, respectively. The inputs of the ANFIS model predictor of first wave arrival time were selected as pool elevation at failure, final breach bottom elevation and distance from the dam. The training/testing ratio was determined as 75/25%. The dataset contains 140 samples which was divided as 105 samples for training phase and 35 samples for testing phase. Coefficient of correlation and root mean squared error values expressing the success of the ANFIS model developed for the analysis of first wave arrival time parameter in the test phase are 0.9828 and 3.6216, respectively. The statistical parameters prove that the analysis success of the models are robust. The success of soft computing methods in the analysis of dam failure problem has been shown to be a useful tool in the management of real disaster events. To put it more clearly, some applications that contain algorithms working in real time can be established for such possible disasters. These applications can reduce the extent of the disaster by using them as practical solution tools in such disaster times. It is possible to learn the failure mode of any dam and the mechanism of breach development as soon as the event occurs. However, this is too late for any flood parameters to be analysed. In such events, there are only minutes for the right actions to be taken. For example, when the Manavgat Dam collapses for some reason, how long the first wave will arrive at any location is important for evacuation, or knowing the possible maximum water elavation at any point means the elevation at which people must climb. In such limited times, it takes time to perform numerical analysis according to the emerging flood hydrograph, but algorithms trained before the event according to various scenarios can provide practical solutions at the time of the event and provide information to the relevant people.
  • Öge
    Numerical modelling of wave induced soil liquefaction around buried pipelines and cables
    (Graduate School, 2022-01-17) Yılmaz, Selahattin Utku ; Kırca, Özgür V. Ş. ; 501181532 ; Hydraulics and Water Resources Engineering
    In this thesis at first, the concept of soil liquefaction is researched in terms of physics, and the reasons & consequences of this phenomenon are investigated. Besides, the conditions (occurrence in which type of loadings, in which type of soils, and so on.) that cause this phenomenon is mentioned. In short, there are two different types of liquefaction failure of soil; residual and momentary liquefaction. Then, both type of liquefaction is mentioned. However, in this thesis, the residual liquefaction of soil is investigated for the design aspects of submarine pipelines and offshore cables. Besides, the effect of this phenomenon on the structures especially buried objects is scrutinized in many ways. Then generally, it is stated that the buried objects heavier than the liquefied soil sink deeper in the soil, while lighter objects float to the surface when the soil is liquefied. These are called sinking and floatation failures too. In addition, numerous articles and research about this failure are reviewed in the literature. In these researches, the mechanism of the marine soil, the liquefaction/compaction process of the soil (life cycle of the soil), the stress-strain relationship of the soil under loadings, and the relevant conditions (wave or earthquake loading, soil type, so on) are stated in this thesis as theoretical (with analytical & numerical models) and experimental works. Particularly, the disturbance effect on the soil by buried objects such as offshore pipelines/cables is scrutinized comprehensively based on the relevant articles.
  • Öge
    Disaggregation of future climate projection data to generate future rainfall intensity-duration-frequency curves to assess climate change impacts
    (Graduate School, 2021-04-03) Tayşi, Hüsamettin ; Özger, Mehmet ; 501171524 ; Hydraulics and Water Resources Engineering ; Hidrolik ve Su Kaynakları Mühendisliği
    A heavy increase in urbanization, industrialization, and population is causing an increase in emissions of greenhouse gases (GHG). Increment in GHG emissions causes variations in the atmosphere and in climate conditions. Climate change is one of the most serious reasons for extreme climate events such as high global temperature, extremely heavy rainfall events, and high evapotranspiration. According to many studies, climate change impacts will intensify in the future. As a result of this, heavy rainfall events tend to enhance. In our case extreme rainfall events, which are responsible for flooding events, were considered. Since flooding is influencing urban areas acutely, controlling and management of flooding is a major necessity for cities. Intensity-Duration-Frequency (IDF) curves play a huge role in representing rainfall characteristics by linking the intensity, duration, and frequency of rainfall. These curves give the expected rainfall intensity in duration (5, 10, 15, 30 min.; 1, 2, 3, 4, 5, 6, 8, 12, 18, and 24 hours) and in a return period (2, 10, 25, 50 and 100 years). Hence, IDF curves are used in many water-related applications such as water management, designing of infrastructure, drainage, culverts, gutters, and also flood forecasting. However, current IDF curves are generated based on historical rainfall events. These IDF curves are considered stationary since they only consider historical events. The increase in GHG leads to variations in climate, especially in rainfall behaviors. Thus, IDF curves must catch the changes in rainfall intensities, in other words, they must be non-stationary and time-varying based. This study updates IDF curves to assess future climate conditions. For the study, six meteorological stations from Istanbul (Florya, Goztepe, Sariyer, Sile, Omerli and Canta) were selected as study areas. As a consequence, cities will be prepared for upcoming extreme events, hence possible damages will be decreased. A Global Climate Model (GCM) HadGEM2-ES generated under RCP4.5 and RCP8.5 scenarios were used in the study to represent future rainfall in daily form. 1-min and hourly rainfall data were provided by the Turkish State Meteorological Service (TSMS). HYETOS disaggregation model was applied to both historical and future rainfall data to obtain sub-hourly data (also hourly for future rainfall). Since GCMs are not suitable to use directly due to biases, the distribution mapping method was selected as a bias-correction method. The Gumbel model was applied to annual maximum rainfall to generate IDF curves. Finally, historical and future IDF curves, IDF curves generated under RCP4.5 and RCP8.5, and also IDF curves generated using disaggregated historical data and observed IDF curves provided by TSMS were compared. The study concluded that rainfall intensities increase under RCP4.5 and RCP8.5 scenarios compared to historical IDF curves. Besides, RCP8.5 has higher rainfall intensities when compared to rainfall intensities of RCP4.5.