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.
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: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.
In this study, missing value analysis and homogeneity tests were conducted for 267 precipitation stations throughout Turkey. For this purpose, the monthly and annual total precipitation records at stations operated by Turkish State Meteorological Service (DMI) from 1968 to 1998 were considered. In these stations, precipitation records for each month was investigated separately and the stations with missing values for too many years were eliminated. The missing values of the stations were completed by Expectation Maximization (EM) method by using the precipitation records of the nearest gauging station. In this analysis, 38 stations were eliminated because they had missing values for more than 5 years, 161 stations had no missing values and missing precipitation values were completed in the remaining 68 stations. By this analysis, annual total precipitation data were obtained by using the monthly values. These data should be hydrologically and statistically reliable for later hydrological, meteorological, climate change modelling and forecasting studies. For this reason, Standard Normal Homogeneity Test (SNHT), (Swed-Eisenhart) Runs Test and Pettitt homogeneity tests were applied for the annual total precipitation data at 229 gauging stations from 1968 to 1998. The results of each of the testing methods were evaluated separately at a significance level of 95% and the inhomogeneous years were determined. With the application of the aforementioned methods, inhomogeneity was detected at 50 stations of which the natural structure was deteriorated and 179 stations were found to be homogeneous.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.