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Öğe Applications of innovative polygonal trend analyses to precipitation series of Eastern Black Sea Basin, Turkey(Springer Wien, 2022) Hirca, Tugce; Turkkan, Gokcen Eryilmaz; Niazkar, MajidExamining historical variations of hydroclimatic variables can provide crucial information about changes of water resources in a water cycle. In this study, the Mann-Kendall (MK) and Innovative Polygon Trend Analysis (IPTA) methods were applied to 56-year precipitation data collected at 8 measuring stations. These stations are located in Eastern Black Sea Basin (EBSB), which has a significant amount of annual precipitation and hydroelectric potential in Turkey. This study has two objectives: (1) investigating possible changes in the monthly precipitation and (2) comparing the results achieved by a classical (MK) and one of the latest trend analysis methods presented in the literature (IPTA). Based on the results, MK achieved no trend for most of months, while it reached an increasing trend for March at most of the stations. Likewise, IPTA determined an increasing trend for March precipitation. However, an increasing/decreasing trend was obtained by IPTA for most of the months and stations. In other words, comparing the trend analysis results obtained by IPTA and MK indicates a significant discrepancy between the numbers of months with detected trends primarily because the former is relatively more sensitive in trend identification. To be more precise, IPTA and MK determined trends in approximately 81.25% and 12.5% of all months, respectively. Furthermore, the former identified quite the same trends in every month which the latter reported a trend. Moreover, the polygon of the mean and standard deviation graphs developed by IPTA provides a year cycle, which brings about useful information for water utility sectors and decision makers of the study area. Finally, the findings of this study contribute to a large amount of research that attempts to explore spatio-temporal variations of hydroclimatic variables around the globe not only to enhance humans' knowledge about changes in a water cycle but also assess climate change impacts.Öğe Assessment of Different Methods for Estimation of Missing Rainfall Data(Springer, 2024) Hirca, Tugce; Turkkan, Goekcen EryilmazMissing data is a common problem encountered in various fields, including clinical research, environmental sciences and hydrology. In order to obtain reliable results from the analysis, the data inventory must be completed. This paper presents a methodology for addressing the missing data problem by examining the missing data structure and missing data techniques. Simulated datasets were created by considering the number of missing data, missing data pattern and missing data mechanism of real datasets containing missing values, which are often overlooked in hydrology. Considering the missing data pattern, the most commonly used methods for missing data analysis in hydrology and other fields were applied to the created simulated datasets. Simple imputation techniques and expectation maximization (EM) were implemented in SPSS software and machine learning techniques such as k-nearest neighbor (kNN), together with the hot-deck were implemented in the Python programming language. In the performance evaluation based on error metrics, it is concluded that the EM method is the most suitable completion method. Homogeneity analyses were performed in the Mathematica programming language to identify possible changes and inconsistencies in the completed rainfall dataset. Homogeneity analyses revealed that most of the completed rainfall datasets are homogeneous at class 1 level, consistent and reliable and do not show systematic changes in time.Öğe Comparison of Statistical Methods to Graphical Method in Precipitation Trend Analysis, A Case Study: Coruh Basin, Turkey(Springer Int Publ Ag, 2022) Hirca, Tugce; Turkkan, Gokcen EryilmazIt is a known fact that the precipitation characteristics will become irregular as a result of climate change resulting from global warming. Trend analysis is one of the most effective methods of observing the effects of climate change on precipitation. This study compares the changes in precipitation with traditional trend analysis methods and graphical method (divided into subcategories using the Z-Score Index). Some preliminary analyzes (missing data estimation, homogeneity check, autocorrelation, and removal of the autocorrelation), which are lacking in many studies in the literature, have been performed. In this context, the monthly total precipitation data of the precipitation stations belonging to the Coruh Basin, one of the most important basins of Turkey, for the period 1972-2011 were used. As a result of the study, it was determined that all the stations' data were homogeneous, and 92% of them were at the Class A level. While 100% trend is determined in Innovative Trend Analysis in total annual precipitation, this rate was just 40% at Mann-Kendall and Spearman's rho at 95% confidence. An increasing trend was determined in the high group of total spring precipitation at all stations.Öğe Drought analysis using innovative trend analysis and machine learning models for Eastern Black Sea Basin(Springer Wien, 2024) Niazkar, Majid; Piraei, Reza; Turkkan, Gokcen Eryilmaz; Hirca, Tugce; Gangi, Fabiola; Afzali, Seied HoseinThis study aims to assess the Eastern Black Sea Basin drought conditions. For this purpose, the trend changes in SPI values of 6, 9, 12, and 24 months using innovative trend analysis were examined. Additionally, four machine learning models, including Multiple Linear Regression, Artificial Neural Networks, K Nearest Neighbors, and XGBoost Regressor, are employed to forecast SPI with rainfall data between 1965 and 2020 from eight rainfall stations. The input data for each model was SPI values from lead times of 1 to 6, resulting into 768 unique scenarios. The ML models estimated SPI values better as the SPI duration increased, with the 24-month SPI showing the highest accuracy. The results of SPI forecast indicated that the optimal model and number of input variables varied for each SPI and station, indicating that further studies are required to improve SPI predictions.Öğe The investigation of flood risk perception as a quantitative analysis from socio-demographic perspective(Springer, 2021) Eryilmaz Turkkan, Gokcen; Hirca, TugceThe priority of flood management planning is physical victimization and focuses on taking structural measures. Although this approach is an accurate approach, more information is needed in implementing efficient precautionary and planning decisions. It is an indisputable fact that the existence of nothing that is not sustainable in nature cannot continue. Hence, it is necessary to implement a planning decision suitable for the structure of the population living in the region so that the continuity of the policies to be carried out against natural hazards of hydrometeorological origin such as a flood is ensured. How the socio-demographic structures affect the flood risk perception of 245 people living in the city center of Bayburt is examined in this study. It is the first research conducted for the province of Bayburt for this perspective. The participants were asked to fill a questionnaire containing 24 items and consisting of 2 sections. T test and one-way ANOVA (one-way analysis of variance) statistical methods were used to ascertain the difference between the responses of the participants to the questionnaire, based on their demographic structure. As the result of the study, significant differences were observed between the expressions depicting flood risk perception and the participant's age, income levels and educational background. In addition, it has been noted that there is a positive relationship between education and income levels and flood risk perception.
