Drought causes huge losses in agriculture and has many negative influences on natural ecosystems. In this study, the applicability of Adaptive Neuro-Fuzzy Inference System (ANFIS) for drought forecasting and quantitative value of drought indices, the Standardized Precipitation Index (SPI), is investigated. For this aim, 10 rainfall gauging stations located in Central Anatolia, Turkey are selected as study area. Monthly mean rainfall and SPI values are used for constructing the ANFIS forecasting models. For all stations, data sets include a total of 516 data records measured between in 1964 and 2006 years and data sets are divided into two subsets, training and testing. Different ANFIS forecasting models for SPI at time scales 1-12 months were trained and tested. The results of ANFIS forecasting models and observed values are compared and performances of models were evaluated. Moreover, the best fit models have been also trained and tested by Feed Forward Neural Networks (FFNN). The results demonstrate that ANFIS can be successfully applied and provide high accuracy and reliability for drought forecasting.

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Abstract. The use of Artificial Intelligence methods is becoming increasingly common in the modeling and forecasting of hydrological and water resource processes. In this study, applicability of Adaptive Neuro Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) methods, Generalized Regression Neural Networks (GRNN) and Feed Forward Neural Networks (FFNN), and Auto-Regressive (AR) models for forecasting of daily river flow is investigated and Seyhan River and Cine River was chosen as case study area. For the Seyhan River, the forecasting models are established using combinations of antecedent daily river flow records. On the other hand, for the Cine River, daily river flow and rainfall records are used in input layer. For both stations, the data sets are divided into three subsets, training, testing and verification data set. The river flow forecasting models having various input structures are trained and tested to investigate the applicability of ANFIS and ANN and AR methods. The results of all models for both training and testing are evaluated and the best fit input structures and methods for both stations are determined according to criteria of performance evaluation. Moreover the best fit forecasting models are also verified by verification set which was not used in training and testing processes and compared according to criteria. The results demonstrate that ANFIS model is superior to the GRNN and FFNN forecasting models, and ANFIS can be successfully applied and provide high accuracy and reliability for daily river flow forecasting.

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.