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Öğe Application of Third-Order Schemes to Improve the Convergence of the Hardy Cross Method in Pipe Network Analysis(Hindawi Ltd, 2021) Niazkar, Majid; Turkkan, Gokcen EryilmazIn this study, twenty-two new mathematical schemes with third-order of convergence are gathered from the literature and applied to pipe network analysis. The presented methods were classified into one-step, two-step, and three-step schemes based on the number of hypothetical discharges utilized in solving pipe networks. The performances of these new methods and Hardy Cross method were compared by solving a sample pipe network considering four different scenarios (92 cases). The results show that the one-step methods improve the rate of convergence of the Hardy Cross method in 10 out of 24 cases (41%), while this improvement was found to be 39 out of 56 cases (69.64%) and 5 out of 8 cases (62.5%) for the two-step and three-step methods, respectively. This obviously indicates that the modified schemes, particularly the three-step methods, improve the performance of the original loop corrector method by taking lower number of iterations with the compensation of relatively more computational efforts.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 Artificial Intelligence Models for Estimating Lengths of Gradually Varied Flow Profiles(Wiley-Hindawi, 2021) Niazkar, Majid; Mishi, Farshad Hajizadeh; Turkkan, Gokcen EryilmazThe study of water surface profiles is beneficial to various applications in water resources management. In this study, two artificial intelligence (AI) models named the artificial neural network (ANN) and genetic programming (GP) were employed to estimate the length of six steady GVF profiles for the first time. The AI models were trained using a database consisting of 5154 dimensionless cases. A comparison was carried out to assess the performances of the AI techniques for estimating lengths of 330 GVF profiles in both mild and steep slopes in trapezoidal channels. The corresponding GVF lengths were also calculated by 1-step, 3-step, and 5-step direct step methods for comparison purposes. Based on six metrics used for the comparative analysis, GP and the ANN improve five out of six metrics computed by the 1-step direct step method for both mild and steep slopes. Moreover, GP enhanced GVF lengths estimated by the 3-step direct step method based on three out of six accuracy indices when the channel slope is higher and lower than the critical slope. Additionally, the performances of the AI techniques were also investigated depending on comparing the water depth of each case and the corresponding normal and critical grade lines. Furthermore, the results show that the more the number of subreaches considered in the direct method, the better the results will be achieved with the compensation of much more computational efforts. The achieved improvements can be used in further studies to improve modeling water surface profiles in channel networks and hydraulic structure designs.Öğe Assessment of Three Mathematical Prediction Models for Forecasting the COVID-19 Outbreak in Iran and Turkey(Hindawi Ltd, 2020) Niazkar, Majid; Eryilmaz Turkkan, Gokcen; Niazkar, Hamid Reza; Turkkan, Yusuf AlptekinCOVID-19 pandemic has become a concern of every nation, and it is crucial to apply an estimation model with a favorably-high accuracy to provide an accurate perspective of the situation. In this study, three explicit mathematical prediction models were applied to forecast the COVID-19 outbreak in Iran and Turkey. These models include a recursive-based method, Boltzmann Function-based model and Beesham's prediction model. These models were exploited to analyze the confirmed and death cases of the first 106 and 87 days of the COVID-19 outbreak in Iran and Turkey, respectively. This application indicates that the three models fail to predict the first 10 to 20 days of data, depending on the prediction model. On the other hand, the results obtained for the rest of the data demonstrate that the three prediction models achieve high values for the determination coefficient, whereas they yielded to different average absolute relative errors. Based on the comparison, the recursive-based model performs the best, while it estimated the COVID-19 outbreak in Iran better than that of in Turkey. Impacts of applying or relaxing control measurements like curfew in Turkey and reopening the low-risk businesses in Iran were investigated through the recursive-based model. Finally, the results demonstrate the merit of the recursive-based model in analyzing various scenarios, which may provide suitable information for health politicians and public health decision-makers.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.
