Mahmud Güngör

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 Catchments, 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.

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.

Reduction of water losses by rehabilitation of water distribution network

Physical or real losses may be indicated as the most important component of the water losses occurring in a water distribution network (WDN). The objective of this study is to examine the effects of piping material management and network rehabilitation on the physical water losses and water losses management in a WDN. For this aim, the Denizli WDN consisting of very old pipes that have exhausted their economic life is selected as the study area. The fact that the current network is old results in the decrease of pressure strength, increase of failure intensity, and inefficient use of water resources thus leading to the application of the rehabilitation program. In Denizli, network renewal works have been carried out since the year 2009 under the rehabilitation program. It was determined that the failure rate at regions where network renewal constructions have been completed decreased down to zero level. Renewal of piping material enables the minimization of leakage losses as well as the failure rate. On the other hand, the system rehabilitation has the potential to amortize itself in a very short amount of time if the initial investment cost of network renewal is considered along with the operating costs of the old and new systems, as well as water loss costs. As a result, it can be stated that renewal of piping material in water distribution systems, enhancement of the physical properties of the system, provide significant contributions such as increase of water and energy efficiency and more effective use of resources.