Three-dimensional imputation of missing monthly river flow data

Imputation of missing data is a critical part of accurate data analysis and modeling. This paper presents 3D imputation as a new data-driven methodology to estimate missing values in time series data. The method is based on the assumption that all the observed data in a time series are related with each other and with data of the some other series. The available data is placed in a three-dimensional space so that the increasing or decreasing relationships between the observed data are appropriately represented. For the estimation of each missing value, the method searches and determines the best possible group of estimator data within the data space. Different data groups are found and used for the estimations of each individual group of missing data. The method is validated by removing and estimating all the observed monthly flow data of Saraykoy station on Buyuk Menderes River in Turkey. Data of the downstream Burhaniye station constituted the second data layer in the model. High correlation values were obtained for all years between observations and estimations and the missing data of Saraykoy station was also estimated by using the proposed method. (C) 2016 Sharif University of Technology. All rights reserved

associations in time series data

The widely used Pearson's correlation coefficient calculated for assessing the linear relationship between two variables might produce misleading results especially in the comparison of periodic variables. A single correlation coefficient provides a measure of the overall dependence structure and generally might not be sufficient for assessing local differences between the variables (e.g. associations between each individual year might vary in hydrologic series). The reason for this deficiency is the consideration of the averages of the whole series while ignoring the variations of the local averages (e.g. annual averages or long year averages of months) throughout the observations. This study presents a two-dimensional horizontal (row wise) and vertical (column wise) correlation calculation approach where the compared series are considered as two-dimensional matrices in which each row represents a sub-period (e.g. one calendar year of the precipitation data) of the investigated time series data. The method applies a normalization procedure by considering the averages of all rows (namely local averages) for calculating the horizontal correlation and the averages of all columns for calculating the vertical correlation instead of considering the averages of the whole matrices. This enables a separate determination of the degree of relationships between the rows and columns of the compared data matrices by using the horizontal and vertical variance and covariance values that constitute the base of the two-dimensional correlation. The method is applied on 14 different linearly varying hypothetical matrices, 6 matrices for testing the influence of seasonal and inter-annual variations and the monthly total precipitation records of 6 stations in southwest Turkey. The results have shown that the developed correlation approach assesses the two-dimensional behaviour of time series data like precipitation and provides a measure which enables separate assessment of the contributions from the seasonal cycle vs. inter-annual variability in the association between two time series

Frequency Based Prediction of Buyuk Menderes Flows

In this study, a new method for the data driven prediction of interrelated and chaotic time series data showing seasonal fluctuations is proposed. The method produces predictions based on the temporal and quantitative relationships among the available data related with the frequencies of the value ranges of observed data. The method, which is called frequency based prediction, has a general approach and requires no testing/validation/adjustment/weight determination steps. The developed method is used for predicting 9050 monthly total flow observations of 34 stations on Buyuk Menderes River and for infilling 1210 missing data. High correlations obtained between the observations and predictions for all stations show that the proposed method is successful in the prediction of streamflow data

subregion of Turkey with L-moments

In this study, a regional frequency analysis of the annual maximum series of flood data observed at 21 gauged sites in the lower west Mediterranean river basin, Turkey has been carried out using, the index flood L-moments method. Based on the analysis, the lower west Mediterranean river basin is shown to be possibly heterogeneous, and Pearson Type III distribution is found to be the most accurate way to describe the distribution of the floods within this sub-region. For estimation of floods of various return periods for ungauged catchments of the sub-region, a regional flood frequency relationship has been developed using the L-moments based on the Pearson Type III distribution. The results are evaluated on the basis of relative root mean square error, and relative bias through the use of the Monte Carlo simulation

Meteorological drought analysis case study: Central Anatolia

Drought is one of the major disasters which might have consequences like hunger and poverty. The droughts depend on many factors including climatic and regional properties, soil type, population increase and environmental degradation. The complex character of drought makes it difficult to define. Hence, various specific criteria must be defined and used for the evaluated basin, region or territory to determine drought. In this study, several drought analysis methods are performed on the Central Anatolian Region in Turkey where has survived a severe drought. In comparative analysis, Palmer Drought Severity Index (PDSI), Erinc and De Martonne methods were used. The evaluated data consist of the observed monthly mean precipitation and temperature data of 13 selected meteorology stations in the region. The observed data in between 1965-2006 periods were evaluated for all stations. Thus, the distribution of dry and wet periods is investigated at monthly time scale. The comparative results show that PDSI index indicates more humid conditions than Erinc and De Martonne indices. Nevertheless, the results verify that the region is still in danger of severe drought

Mediterranean river basins in Turkey

Estimation of probability distribution and return periods of flood peak flows is needed for the planning, design, and management of flood control. Much of the research on at-site and regional flood frequency analysis has focused on the determination of the best probability distribution. The first objective of this study was to determine, evaluate and compare the goodness of fit of popular probability distribution functions (PDFs) to sequences of annual maximum stream-flows measured in West Mediterranean river basins of Turkey. Besides Gumbel distribution, which is generally preferred because of its simplicity and generality in extreme hydrologic data, distributions like Pareto, Loglogistic, Pearson Type III, Log-Pearson Type III, Log-normal with two and three parameters, and Generalized Extreme Value distributions are applied to the series of annual floods with time periods ranging from 20 to 61 years for 37 gauging stations. Another objective of the study was to compare and evaluate the parameter estimation methods and goodness of fit tests for the basins. For parameter estimation, the traditionally used method of moments and, recently widely used, that of probability weighted moments were used. To make an evaluation of the suitability of the parameters obtained by both methods to the data, detailed chi-square (parametric) tests were applied twice with equal-length intervals, equal-probability intervals and Kolmogorov-Smirnov (non-parametric) goodness-of-fit tests.The results demonstrated that when chi-square goodness of fit test is applied for both parameter estimation methods (moments and probability weighted moment methods), Gumbel probability distribution was obtained as the best fitting one to the floods in West Mediterranean river basins in Turkey, according to chi-square test with equal-probability and equal-length class intervals for both of the methods.Besides, the application of chi-square goodness of fit test for both parameter estimations with average chi-square approach resulted in Log-Pearson Type III for both with equal class intervals as optimal distribution. Similar results were obtained for chi-square distribution with equal probability approach. Log-Pearson Type III distribution was the best suitable one for each of the parameter estimation methods in Kolmogorov-Smirnov goodness of fit test. These results indicated that it may be more appropriate to use Log-Pearson Type III distribution instead of the widely used Gumbel distribution for probability distribution modeling of extreme values in West Mediterranean river basins

Adaptive Neuro-Fuzzy Inference System for drought forecasting

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

Applicability of apportionment entropy as a drought index

Drought is one of the most important natural disasters. The main aim of the present study is to investigate the applicability of the Apportionment Entropy (AE) as a drought index. For this purpose, the Standard Precipitation Index (SPI) and the AE method as a drought indices were investigated and compared for monthly precipitation data in Northern Aegean Region. In the presented study, 29 gauging stations with long term observations are evaluated in Northern Aegean Region. The results of the study showed that PE method can be used as a drought index and the analysis of drought results contributed to the identification of drought periods for the Northern Aegean Region

Comparative study of impact strength of six acrylic denture resins

The aim of this in vitro study was to compare the impact strength of three kinds of heat polymerised acrylic resin: a microwave polymerised acrylic resin, a visible light cured resin and a self-cured acrylic resin. From a group of six different materials, a total of 60 specimens (75 x 10 x 3 mm) were fabricated, 10 from each material. The impact strength was evaluated using the Charpy method. The test was performed at room temperature in an impact testing machine (Zwick pendulum impact tester; Zwick GmbH & Co. KG, Ulm, Germany) of a capacity of 0-7.5 J scale; the specimens were fractured. For statistical analysis, Kruskal-Wallis test followed by Dunn's multiple comparison test was used. The impact strength values exhibited statistically significant differences among acrylic resin groups (p=0.0001). High impact strength acrylic resin showed the highest mean impact strength value among the materials tested