Civil Engineering / İnşaat Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/13
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Article Citation - WoS: 3Citation - Scopus: 3Ensemble and Optimized Hybrid Algorithms Through Runge Kutta Optimizer for Sewer Sediment Transport Modeling Using a Data Pre-Processing Approach(Elsevier, 2023) Safari, Mir Jafar Sadegh; Gül, Enes; Dursun, Ömer Faruk; Tayfur, GökmenUncontrolled sediment deposition in drainage and sewer systems raises unexpected maintenance expenditures. To this end, implementation of an accurate model relying on effective parameters involved is a reliable benchmark. In this study, three machine learning techniques, namely extreme learning machine (ELM), multilayer perceptron neural network (MLPNN), and M5P model tree (M5PMT); and three optimization approaches of Runge Kutta (RUN), genetic algorithm (GA), and particle swarm optimization (PSO) are applied for modeling. The optimization and ensemble hybridization approaches are applied in the modeling procedure. For the case of hybrid optimized models, the ELM and MLPNN models are hybridized with RUN, GA, and PSO algorithms to develop six hybrid models of ELM-RUN, ELM-GA, ELM-PSO, MLPNN-RUN, MLPNN-GA, and MLPNN-PSO. Ensemble hybrid models are developed through coupling the ELM and MLPNN models with the M5PMT algorithm. The data pre-processing approach is applied to find the best randomness characteristic of the utilized data. Results illustrate that the RUN-based hybrid models outperform the GA- and PSO-based counterparts. Although the MLPNN-RUN and MLPNN-M5PMT hybrid models generate better results than their alternatives, MLPNN-M5PMT slightly outperforms MLPNN-RUN model with a coefficient of determination of 0.84 and a root mean square error of 0.88. The current study shows the superiority of the ensemble-based approach to the optimization techniques. Further investigation is needed by considering alternative optimization techniques to enhance sediment transport modeling. © 2023 International Research and Training Centre on Erosion and Sedimentation/the World Association for Sedimentation and Erosion ResearchArticle Citation - WoS: 7Citation - Scopus: 5Meteorological Drought and Trend Effects on Transboundary River Basins in Afghanistan(Springer, 2023) Hayat, Ehsanullah; Tayfur, GökmenAfghanistan, as a landlocked country located within central and southwestern Asia, has an arid to semi-arid climate. Most of the people are involved in agricultural activities, and a major part of the country's gross domestic product depends on agriculture, but the country has the lowest water storage capacity. Consecutive periods of drought and rapid snowmelt due to climate change have made it more challenging for suitable water resource management practices. This study investigates the historical meteorological drought characteristics across the whole country by employing the Reconnaissance Drought Index for the period 1979-2019 using data from 55 meteorological stations. Trends in precipitation and temperature are also investigated using the Mann-Kendall's and the Sen's slope statistical tests. A four-decadal countrywide drought map is generated. Extreme and severe droughts were observed in 1999 and 2000 across the whole country. Moderate drought events have started to occur with a frequency of 3 to 5 years since 1999. The decadal annual rainfall values in each river basin indicate that rainfall has decreased in the last two decades with a significant decline in 1999-2008. The trends of increase in temperature and decrease in precipitation are indications of rapid climate change in the country, especially in the south, west, and southwest regions. Due to the intensity and frequency of the droughts, river flow rates have decreased; and therefore, there is a need for the upstream and downstream neighboring countries to come to terms with the phenomenon of a new normal in the hydrological cycle and accordingly revise new water sharing treaties.Book Part Citation - Scopus: 4Application of Fuzzy Logic in Water Resources Engineering(Elsevier, 2022) Tayfur, GökmenThis chapter introduces the fundamentals of fuzzy logic (FL), fuzzy sets, and fuzzy model components such as the fuzzification, the fuzzy rule base, the fuzzy inference engine, and the defuzzification. The processes of the fuzzy model components are presented by working on the examples from the water resources engineering application problems. This chapter also discusses the merits and the shortcomings of the fuzzy modeling. Hydrological processes have inherent source of uncertainty, for which the fuzzy set theory can be an effective solution tool. © 2023 Elsevier Inc. All rights reserved.Book Part Citation - Scopus: 1Developments in Sediment Transport Modeling in Alluvial Channels(Elsevier, 2022) Tayfur, GökmenThis chapter discusses the developments in the mathematical modeling of sediment transport dynamics in alluvial channels. Starting with early experimental and empirical studies, it goes on to treating the processes in 1D, 2D, and 3D uniform sediment transport. Finally, it describes the treatment of the processes in 3D nonuniform sediment transport considering turbulence effects. While introducing the advancements in mathematical modeling of the dynamics, the chapter also discusses the outstanding issues like the treatment of the particle fall velocity, the particle velocity, and sediment transport rate function. © 2023 Elsevier Inc. All rights reserved.Book Part Citation - Scopus: 3Real-Time Flood Hydrograph Predictions Using Rating Curve and Soft Computing Methods (ga, Ann)(Elsevier, 2022) Tayfur, GökmenThis chapter introduces hydraulic and hydrologic flood routing methods in natural channels. It details hydrological flood routing methods of the Rating Curve and Muskingum. Based on the rating curve method (RCM), it presents real-time flood hydrograph predictions using the genetic algorithm (GA-based RCM) model. In addition, it presents how to make real-time flood hydrograph predictions using the artificial neural network (ANN). The chapter briefly introduces the basics of GA and details how to calibrate and validate the GA-based RCM model using measured real-time flood hydrographs. Similarly, after giving the basics of ANN, it shows how to train and test the ANN model using measured hydrographs. Real hydrograph simulations by the RCM, GA-based RCM, and ANN are presented, and merits of each model are discussed. © 2023 Elsevier Inc. All rights reserved.Article Citation - WoS: 4Citation - Scopus: 53d Modelling of Surface Spreading and Underground Dam Groundwater Recharge: Egri Creek Subbasin, Turkey(Springer, 2023) Şahin, Yavuz; Tayfur, GökmenThis study investigated surface spreading and underground dam recharge methods to replenish groundwater in Turkey's Egri Creek Sub-basin of the Kucuk Menderes River Basin. A three-dimensional numerical model was employed for this purpose. Field and lab data are provided to the model for realistic simulations. Pumping test results were used to determine the aquifer parameters. The laboratory works involved sieve analysis, permeability tests, and porosity and water content prediction. The numerical model's boundary conditions were determined from the geological and hydrogeological characteristics of the study area. Initial conditions were expressed regarding water content and pressure head in the vadose zone. The numerical model was satisfactorily validated by simulating water levels in three different pumping wells in the study area. Seven different scenarios, each having a different pool size, were investigated for the surface spreading recharge method. The results showed that a pool size of 30 x 30 m with a 6-m depth basin was the most optimal choice, raising the groundwater level to about 29.3 m. On the other hand, it was found that an underground dam could raise the levels by an average of 9.5 m, which might not be significant to warrant the construction.Article Citation - WoS: 13Citation - Scopus: 12Identification of Groundwater Potential Zones in Kabul River Basin, Afghanistan(Elsevier, 2021) Tani, Hamidullah; Tayfur, GökmenGroundwater (GW) plays a vital role in the socio-economic growth of Kabul River Basin (KRB) in Afghanistan. Since the GW resources in the basin have not been properly managed, there is a need for sound strategies by first identifying the potential GW zones. This study assesses the potential groundwater zones for the KRB using the Geographic Information Systems (GIS) and the Analytic Hierarchy Process (AHP). In this direction, seven different thematic maps of rainfall, lithology, land use/land cover, slope, soil, drainage density, and lineament density are first prepared using the GIS. The AHP is then employed to assess the weights of different themes. Finally, the weighted overlay option in the GIS is used to generate the map of the groundwater potential zones (GWPZ). The Very Good zones are mostly located in the downstream and central parts of the KRB, covering around 1543 km(2) area. The Good and the Poor zones are found to be randomly distributed, covering about 39 444 km(2) and 27 658 km(2), respectively. The Very Poor zones are located in the west, southwest, and in some central parts of the basin, covering about 2272 km(2). It is found that only 18% of the total average annual precipitated water of 6.88 x 10(9) m(3)/year infiltrates into the subsurface and ultimately contributes to recharging of the groundwater.Article Citation - WoS: 29Citation - Scopus: 31Reverse Flood Routing in Rivers Using Linear and Nonlinear Muskingum Models(American Society of Civil Engineers, 2021) Badfar, Meisam; Barati, Reza; Doğan, Emrah; Tayfur, GökmenOne of the key factors for flood modeling and control is the flood hydrograph, which is not always available due to lack of flood discharge observations. In reverse flow routing, hydraulic or hydrological calculations are performed from the downstream end to the upstream end. In the present study, a reverse flood routing approach is developed based on the Muskingum model. The storage function is conceptualized as linear and five different nonlinear forms. The Euler and the fourth-order Runge-Kutta numerical methods are used for solving the storage models. The shuffled complex evolution (SCE) algorithm is used for optimization of the flood routing parameters. The models are calibrated and validated with theoretical and actual hydrographs. The results indicate that the proposed methodology could substantially (up to almost 82%) improve comparison with observed inflows. The practical applicability of the proposed methodology is also validated in real river systems.Article Citation - WoS: 8Citation - Scopus: 9Empirical, Numerical, and Soft Modelling Approaches for Non-Cohesive Sediment Transport(Springer, 2021) Tayfur, GökmenThis paper reviews the modelling approaches and outstanding issues with regard to non-cohesive sediment transport which has been experimentally and numerically studied for many decades owing to its importance to hydraulic structures, morphology and related areas. About 311 papers are reviewed that included laboratory experiments, field observations, and analytical and numerical modelling studies. The reviewed papers cover the period 1938-2020. Of 311, 95 papers are included in this paper. The modeling approaches include empirical, physics-based, spatially averaged, and soft methods. The empirical models have oversimplified the process while the physics-based models are indispensable when the detailed analysis is required. On the other hand, when the objective is to obtain cumulative sediment loads, it would be advantageous to employ the spatial averaging modelling and/or the soft computing methods due to less computational burden and data requirements. The outstanding issues are related to the particle fall velocity, particle velocity, incipient motion, and transport function that require further experimental investigations especially for unsteady non-uniform transport processes.Article Citation - WoS: 27Citation - Scopus: 27Discrepancy Precipitation Index for Monitoring Meteorological Drought(Elsevier, 2021) Tayfur, GökmenWidely employed precipitation drought indices, one way or another, impose probability distribution functions to the data when performing the drought analysis. This may be a plausible approach when the data do not have strong discrepancy which can impede the distribution. The precipitation data in semi-arid and especially in arid regions do have a strong discrepancy due to the sporadic rainfall occurring in such regions. Therefore, in the analysis of the drought for such regions, imposing any probability distribution function to the data could be futile. This study hence developed a new drought index called the Discrepancy Precipitation Index (DPI) for assessing and monitoring the meteorological drought. The method does not impose any probability distribution on the precipitation data. The method is based on the discrepancy of the data with respect to the mean value. The drought classifications are proposed based on the D-score values. Its drought classification ranges are straightforward as those of the Standard Precipitation Index (SPI). The method is applied to assess the meteorological drought at several stations located at different climatic regions such as the arid climate (Mauritania), semi-arid climate (Afghanistan) and the Mediterranean climate (Turkey). The results reveal that the DPI is more representative drought assessment tool for the arid climate regions. At semi-arid climate regions, the DPI can be an alternative drought index to the widely employed (the log-SPI and/or the gamma-SPI) indices. For the Mediterranean climate regions, the DPI can be used together with the other indices. The Discrepancy Measure (DM) is introduced to assess the strength of the discrepancy of the precipitation data series. As the DM of a precipitation series increases, the DPI captures more historical droughts.