Çukurköy (Denizli) dolayının sığ yeraltı suyu hidrojeoloji incelemesi

Cukurkoy graben, located at 13 km southeast from Denizli city, is a side basin with 96.4 km(2) surface drainage area which is connected to the Gokpinar dam catchment area through narrow strait. There are a lots of resources having various discharges on the western slopes of the Honaz mountain in the east of the Cukurkoy basin. These resources were originated from runoff from the ridges which is located between Tavas plain and Cukurkoy basin, and were formed the Gokpinar creek, one of the main tributaries of Buyuk Menderes river. In addition, subsurface waters feed these streams as leaks wherein groundwater level is very shallow level. By the time, local people had opened many boreholes and wells, and they caused to dry out of some continuous resources and streams. Discharge rates of scheduled pumping waters from boreholes are changing dramatically for short distances, because of irregularly located alluvial material. The flow direction of both shallow subsurface and surface waters of the basin outflows toward the northwest that is controlled by surface morphology. These shallow subsurface waters are insufficient for increasing requirements except for small and individual usages

THE FINITE ELEMENT MODEL OF LEAKAGE PHENOMENA UNDER THE SOIL DAMS

Dams can store billions and billions m3 of water in their reservoirs. So leakage phenomenon which threats the stabilitiy of building sould be well examined. In this paper, flow that occurs in the dam's body and at the bottom of dam as a result of leaking water from the earth filled dam's reservoir is examined numericaly. For this purpose, the finite elements model of the phenomenon has been set up with the help of a computer program, the mathematical equation for whole area was calculated, velocity and potential distribution of the flow in the dam's body and at the bottom were obtained. The values of pore water and leak pressure which were previously calculated by graphical method and hand drown flow net can be obtained more precisely by the use of velocity and potential valves obtained by finite element method which is used in this study

Generalized Regression Neural Networks and Feed Forward Neural Networks for prediction of scour depth around bridge piers

In this study, Generalized Regression Neural Networks (GRNN) and Feed Forward Neural Networks (FFNN) approaches are used to predict the scour depth around circular bridge piers. Hundred and sixty five data collected from various experimental studies, are used to predict equilibrium scour depth. The model consisting of the combination of dimensional data involving the input variables is constructed. The performance of the models in training and testing sets are compared with observations. Then, the model is also tested by Multiple Linear Regression (MLR) and empirical formula. The results of all approaches are compared in order to get more reliable comparison. The results indicated that GRNN can be applied successfully for prediction of scour depth around circular bridge piers. © 2008 Elsevier Ltd. All rights reserved

River flow estimation using adaptive neuro fuzzy inference system

Accurate estimation of River flow changes is a quite important problem for a wise and sustainable use. Such a problem is crucial to the works and decisions related to the water resources and management. In this study, an adaptive network-based fuzzy inference system (ANFIS) approach was used to construct a River flow forecasting system. In particular, the applicability of ANFIS as an estimation model for River flow was investigated. To illustrate the applicability and capability of the ANFIS, the River Great Menderes, located the west of Turkey and the most important water resource of Great Menderes Catchment's, was chosen as a case study area. The advantage of this method is that it uses the input-output data sets. Totally 5844 daily data sets collected in 1985-2000 years were used to estimate the River flow. The models having various input structures were constructed and the best structure was investigated. In addition four various training/testing data sets were constructed by cross validation methods and the best data set was investigated. The performance of the ANFIS models in training and testing sets were compared with the observations and also evaluated. The results indicated that the ANFIS can be applied successfully and provide high accuracy and reliability for River flow estimation. © 2006 IMACS

Monthly total sediment forecasting using adaptive neuro fuzzy inference system

Accurate forecasting of sediment is an important issue for reservoir design and water pollution control in rivers and reservoirs. In this study, an adaptive neuro-fuzzy inference system (ANFIS) approach is used to construct monthly sediment forecasting system. To illustrate the applicability of ANFIS method the Great Menderes basin is chosen as the study area. The models with various input structures are constructed for the purpose of identification of the best structure. The performance of the ANFIS models in training and testing sets are compared with the observed data. To get more accurate evaluation of the results ANFIS models, the best fit model structures are also tested by artificial neural networks (ANN) and multiple linear regression (MLR) methods. The results of three methods are compared, and it is observed that the ANFIS is preferable and can be applied successfully because it provides high accuracy and reliability for forecasting of monthly total sediment. © 2009 Springer-Verlag

RIVER BED TOPOGRAPHY IN MEANDERING STREAMS AND ITS RELATIONSHIP WITH LOCAL SCOUR

In Meandering Streams, deformation in bed topography occurs due to secondary flows. This situation significantly increases the local scour depths around hydraulic structures such as bridge piers and dikes, especially at the central angles of ~60º, ~130º and outer boundaries of the channel. Therefore, it is important to determine bed topography in dealing with local scour depths around hydraulic structures and investigate its effect on scour depths. In this study, bed deformations generated before the construction of hydraulic structure in a channel bend of 180o due to only flow are investigated. For this purpose, a series of experiments were made and bed topography was obtained by measuring the bed elevations at each central angle of 5o. As a result of the experimantal study, scour was not much observed in the beginning of the meander because the meterials coming from the upstream replaces the carried material, but it was observed that the scour depth increases because of the lack of incoming material. The generated relative flow depths were determined at flow axis and along the line which was thought to be at a distance of 20 % of channel width from the channel boundary and bed topography generated by the effect of flow was obtained. As a result, it was observed that the parameters which are effective in the generation of bed topography due to flow at meandering channel have the same effect in the generation of local scour when an obstacle is placed and that the bed topography and local scour depths have a very close similarity

Hydrological time-series modelling using an adaptive neuro-fuzzy inference system

Accurate forecasting of hydrological time-series is a quite important issue for a wise and sustainable use of water resources. In this study, an adaptive neuro-fuzzy inference system (ANFIS) approach is used to construct a time-series forecasting system. In particular, the applicability of an ANFIS to the forecasting of the time-series is investigated. To illustrate the applicability and capability of an ANFIS, the River Great Menderes, located in western Turkey, is chosen as a case study area. The advantage of this method is that it uses the input-output data sets. A total of 5844 daily data sets collected from 1985 to 2000 are used for the time-series forecasting. Models having various input structures were constructed and the best structure was investigated. In addition, four various training/testing data sets were built by cross-validation methods and the best data set was obtained. The performance of the ANFIS models in training and testing sets was compared with observations and also evaluated. In order to get an accurate and reliable comparison, the best-fit model structure was also trained and tested by artificial neural networks and traditional time-series analysis techniques and the results compared. The results indicate that the ANFIS can be applied successfully and provide high accuracy and reliability for time-series modelling. Copyright © 2007 John Wiley & Sons, Ltd

AKARSU KIVRIMLARINDA MEYDANA GELEN TABAN TOPOĞRAFYASI VE YEREL OYULMA İLE İLİŞKİSİ

Akarsu kıvrımlarında meydana gelen sekonder akımların etkisiyle taban topoğrafyasında deformasyonlar oluşmaktadır. Bunun sonucunda, akarsu kıvrımlarına yerleştirilecek olan köprü ayakları ve mahmuzlar gibi yapıların etrafında meydana gelen yerel oyulma derinlikleri, kanal kıvrımının ~60º, ~130º merkez açılarında ve kanalın dış kenarında oldukça artırmaktadır. Bu sebeple, su yapıları etrafındaki yerel oyulma derinliklerinin tespiti çalışmalarında taban topoğrafyasının belirlenmesi ve oyulma derinliklerine etkisinin araştırılması oldukça önemli olmaktadır. Bu çalışmada, 180º'lik bir kanal kıvrımında, su yapısı yerleştirilmeden sadece akımdan dolayı meydana gelen taban deformasyonları incelenmiştir. Bunun için bir seri deney yapılarak her 5º'lik merkez açıda kanal enkesiti boyunca taban seviyeleri ölçülmüş ve taban topoğrafyası çıkarılmıştır. Yapılan deneysel çalışmanın sonucunda, kıvrımın girişinde membadan gelen malzeme, taşınan malzemeyi karşıladığından pek fazla oyulma olmadığı, ancak daha sonra gelen malzemenin yetersizliğinden dolayı oyulma derinliğinin arttığı gözlenmiştir. Kanal kenarlarından cidar etkisinin olmadığı düşünülen kanal genişliğinin % 20'si kadar içerideki bir hat boyunca ve akım ekseninde oluşan rölatif akım derinlikleri tesbit edilmiş ve akımın etkisiyle meydana gelen taban topoğrafyası çıkarılmıştır. Sonuç olarak, kıvrımlı kanalda akımın etkisiyle oluşan taban topoğrafyasına etki eden parametrelerin, engel yerleştirildiğinde meydana gelen yerel oyulmaların oluşumunda da aynen etkili olduğu ve taban topoğrafyasıyla yerel oyulma derinliklerinin çok yakın bir benzerlik gösterdiği görülmüştür

Defining Homogeneous Regions for Streamflow Processes in Turkey Using a K-Means Clustering Method

The major problem in ungauged basins for planning and management of water resources projects is to estimate the flood magnitudes and frequencies. The identification of hydrologically homogeneous regions is one of the most important steps of regional frequency analysis. In this study, K-Means clustering method is applied to classify the maximum annual flows and identify the hydrologically homogeneous groups. For this aim, the annual maximum river flows, coefficient of variation and skewness of annual maximum river flows, latitude and longitude at 117 stations operated by the General Directorate of Electrical Power Resources Survey and Development Administration throughout Turkey are used. The optimal number of groups was determined as seven. Regional homogeneity test based on L-moments method is applied to check homogeneity of these seven regions identified by clustering analysis. The results show that regions defined by K-Means method can be used for regional flood frequency analysis. According to the results, K-Means method is recommended to identify the hydrologically homogeneous regions for regional frequency analysis. © 2013 King Fahd University of Petroleum and Minerals