Browsing by Author "Aras, Egemen"

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Estimating suspended sediment load with multivariate adaptive regression spline, teaching-learning based optimization, and artificial bee colony models
(Elsevier, 2018-10-15) Yılmaz, Banu; Aras, Egemen; Nacar, Sinan; Kartal, Murat; Uludağ Üniversitesi/Mühendislik Fakültesi/İnşaat Mühendisliği Bölümü.; 0000-0003-0897-4742; AAZ-6851-2020; 24471611900
The functional life of a dam is often determined by the rate of sediment delivery to its reservoir. Therefore, an accurate estimate of the sediment load in rivers with dams is essential for designing and predicting a dam's useful lifespan. The most credible method is direct measurements of sediment input, but this can be very costly and it cannot always be implemented at all gauging stations. In this study, we tested various regression models to estimate suspended sediment load (SSL) at two gauging stations on the Coruh River in Turkey, including artificial bee colony (ABC), teaching-learning-based optimization algorithm(TLBO), and multivariate adaptive regression splines (MARS). These models were also compared with one another and with classical regression analyses (CRA). Streamflow values and previously collected data of SSL were used as model inputs with predicted SSL data as output. Two different training and testing dataset configurations were used to reinforce the model accuracy. For the MARS method, the root mean square error value was found to range between 35% and 39% for the test two gauging stations, which was lower than errors for other models. Error values were even lower (7% to 15%) using another dataset. Our results indicate that simultaneous measurements of stream flow with SSL provide the most effective parameter for obtaining accurate predictive models and that MARS is the most accurate model for predicting SSL. (C) 2018 Elsevier B.V. All rights reserved.
A laboratory scale investigation of manning roughness coefficient in open channel bed with different grain size and slopes
(Bursa Uludağ Üniversitesi, 2023-07-11) Yılmaz, Damla; Aras, Egemen; Vaheddoost, Babak
Efforts for getting the maximum efficiency from the existing water resources and to implement new projects are getting more attention these days. Determining the flow resistance for the project design and control process in open channels requires sophisticated applications. It is usually essential to be aware of the characteristics of the channel and flow to determine the hydraulic roughness, which represents the resistance of the flow. Hence, empirical calculation and evaluation of the hydraulic roughness will support future design and planning processes. In this study, four different particle sizes (d50= 28mm, 17.5mm, 4mm, and 1.75mm) that were fixed on blocks were used. These particle sizes were then used as the bed covering together with, three different horizontal bed slopes, and flow rates in the experiments to determine the associated Manning roughness, n. During the experiment, Froude number values were examined and it was determined that, in 32 experiments the flow regime can be considered as subcritical. Alternatively, the Lotter method was used to confirm the roughness values obtained by the Manning equation. It was concluded that the roughness values obtained by the selected methods have good concordance with each other.
Prediction of suspended sediment loading by means of hybrid artificial intelligence approaches
(Springer International Publishing Ag, 2019-12-01) Yılmaz, Banu; Aras, Egemen; Nacar, Sinan; Kankal, Murat; KANKAL, MURAT; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi; 0000-0003-0897-4742; AAZ-6851-2020
The main aim of the research is to use the artificial neural network (ANN) model with the artificial bee colony (ABC) and teaching-learning-based optimization (TLBO) algorithms for estimating suspended sediment loading. The stream flow per month and SSL data obtained from two stations, Inanli and Altinsu, in Coruh River Basin of Turkey were taken as precedent. While stream flow and previous SSL were used as input parameters, only SSL data were used as output parameters for all models. The successes of the ANN-ABC and ANN-TLBO models that were developed in the research were contrasted with performance of conventional ANN model trained by BP (back-propagation). In addition to these algorithms, linear regression method was applied and compared with others. Root-mean-square and mean absolute error were used as success assessing criteria for model accuracy. When the overall situation is evaluated according to errors of the testing datasets, it was found that ANN-ABC and ANN-TLBO algorithms are more outstanding than conventional ANN model trained by BP.
Spatial forecasting of dissolved oxygen concentration in the Eastern Black Sea Basin, Turkey
(MDPI, 2020-03-24) Nacar, Sinan; Bayram, Adem; Baki, Osman Tuğrul; Aras, Egemen; Kankal, Murat; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/İnşaat Mühendisliği Bölümü.; AAZ-6851-2020; 24471611900
The aim of this study was to model, as well as monitor and assess the surface water quality in the Eastern Black Sea (EBS) Basin stream, Turkey. The water-quality indicators monitored monthly for the seven streams were water temperature (WT), pH, total dissolved solids (TDS), and electrical conductivity (EC), as well as luminescent dissolved oxygen (LDO) concentration and saturation. Based on an 18-month data monitoring, the surface water quality variation was spatially and temporally evaluated with reference to the Turkish Surface Water Quality Regulation. First, the teaching learning based optimization (TLBO) algorithm and conventional regression analysis (CRA) were applied to three different regression forms, i.e., exponential, power, and linear functions, to predict LDO concentrations. Then, the multivariate adaptive regression splines (MARS) method was employed and three performance measures, namely, mean absolute error (MAE), root means square error (RMSE), and Nash Sutcliffe coefficient of efficiency (NSCE) were used to evaluate the performances of the MARS, TLBO, and CRA methods. The monitoring results revealed that all streams showed the same trend in that lower WT values in the winter months resulted in higher LDO concentrations, while higher WT values in summer led to lower LDO concentrations. Similarly, autumn, which presented the higher TDS concentrations brought about higher EC values, while spring, which presented the lower TDS concentrations gave rise to lower EC values. It was concluded that the water quality of the streams in the EBS basin was high-quality water in terms of the parameters monitored in situ, of which the LDO concentration varied from 9.13 to 10.12 mg/L in summer and from 12.31 to 13.26 mg/L in winter. When the prediction accuracies of the three models were compared, it was seen that the MARS method provided more successful results than the other methods. The results of the TLBO and the CRA methods were very close to each other. The RMSE, MAE, and NSCE values were 0.2599 mg/L, 0.2125 mg/L, and 0.9645, respectively, for the best MARS model, while these values were 0.4167 mg/L, 0.3068 mg/L, and 0.9086, respectively, for the best TLBO and CRA models. In general, the LDO concentration could be successfully predicted using the MARS method with various input combinations of WT, EC, and pH variables.
Suspended sediment load prediction in rivers by using heuristic regression and hybrid artificial intelligence models
(Yıldız Teknik Üniversitesi, 2020-06-01) Yılmaz, Banu; Aras, Egemen; Kankal, Murat; Nacar, Sinan; KANKAL, MURAT; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/İnşaat Mühendisliği Bölümü.; 0000-0003-0897-4742; AAZ-6851-2020
Accurate prediction of amount of sediment load in rivers is extremely important for river hydraulics. The solution of the problem has been become complicated since the explanation of hydraulic phenomenon between the flow and the sediment on the river is dependent many parameters. The usage of different regression methods and artificial intelligence techniques allows the development of predictions as the traditional methods do not give enough accurate results. In this study, data of the flow and suspended sediment load (SSL) obtained from Karsikoy Gauging Station, located on Coruh River in the north-eastern of Turkey, modelled with different regression methods (multiple regression, multivariate adaptive regression splines) and artificial neural network (ANN) (ANN-back propagation, ANN teaching-learning-based optimization algorithm and ANN-artificial bee colony). When the results were evaluated, it was seen that the models of ANN method were close to each other and gave better results than the regression models. It is concluded that these models of ANN method can be used successfully in estimating the SSL.