No abstract

No abstract

ABSTRACT:In this study, missing value analysis and homogeneity tests were applied on the 267 meteorological stations having temperature records throughout Turkey. The monthly and annual mean temperature data of stations operated by the Turkish State Meteorological Service (DMI) for the period 1968-1998 were considered. For each station, each month was analysed separately and the stations with more than 5 years missing values were eliminated. The missing values of the stations were extrapolated by the Expectation Maximization (EM) method using the data of the nearest gauging station (reference station). In consequence of the analysis, annual mean temperature data are obtained by using the monthly values. These data have to be hydrologically/statistically reliable if they are to be used in later hydrological, meteorological, climate change and estimation studies. For this reason, the Standard Normal Homogeneity Test (SNHT), the (Swed-Eisenhart) Runs Test and the Pettitt homogeneity test were applied to detect inhomogeneities in the annual mean temperature series. Each test was evaluated separately and inhomogeneous stations were determined.

Abstract: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.

ABSTRACT:The identification of hydrologically homogeneous regions is one of the most important steps of regional frequency analysis. The hydrologically homogeneous regions should be determined using cluster analysis instead of the geographically close regions or stations. In this study, fuzzy cluster method (Fuzzy C-Means: FCM) is applied to classify the precipitation series and identify the hydrologically homogeneous groups. The choice of appropriate cluster method and the variables that will be used according to the data of the basin is also very important. In the context of this study, total precipitation data of stations operated by National Meteorology Works (DMI) in Turkish basins for cluster analysis are used. The optimal number of groups is determined as six, based on different performance evaluation indexes. Regional homogeneity tests based on L-moments method are applied to check homogeneity of these six regions identified by cluster analysis. Regional homogeneity test results show that regions defined by FCM method are sufficiently homogeneous for regional frequency analysis. According to the results, FCM method is recommended for classifying the precipitation series and for identifying the hydrologically homogenous regions.