Browsing by Author "Kankal, Murat"

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Annual trends of precipitation and temperature in the northwestern part of Turkey using innovative approaches: A holistic and partial study
(Springer Basel Ag, 2023-07-03) San, Murat; Nacar, Sinan; Akçay, Fatma; Kankal, Murat; KANKAL, MURAT; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/İnşaat Mühendisliği Bölümü.; 0000-0003-0897-4742; AAZ-6851-2020
Trend analysis benefits collecting and analyzing reliable data in climate change studies. In this context, long-term temperature and precipitation data analysis, two variables sensitive to climate change, is essential. This study aims to holistically and partially determine the annual trends of precipitation and temperature time series for 1970-2019 at nine selected stations in the Susurluk Basin, Turkey. The innovative trend significance test (ITST), innovative crossing trend analysis (ICTA) method, and Mann-Kendall (MK) test were used to determine holistic trends. Also, partial trends were determined using successive average methodology (SAM). For the precipitation variable, while an increasing trend was determined for the ITST, there was mostly no trend for the other methods. While a strong increasing trend was detected for the temperature according to the ITST and MK methods, no trend was observed in any station according to the ICTA method. According to the SAM results, the maximum trend durations for the peak and trough change points were 4.9 (10.3) and 5.3 (8.4) years, respectively, for precipitation (temperature). The strong temperature trends in the basin will likely continue, requiring precautions against extreme events such as drought.
Artificial intelligence applications in civil engineering
(Hindawi, 2019) Dede, Tayfun; Vosoughi, Ali Reza; Grzywinski, Maksym; Kripka, Moacir; Kankal, Murat; Uludağ Üniversitesi/Mühendislik Fakültesi/İnşaat Mühendisliği.; 0000-0003-0897-4742; AAZ-6851-2020; 24471611900
Daily precipitation performances of regression-based statistical downscaling models in a basin with mountain and semi-arid climates
(Springer, 2023-04-01) Şan, Murat; Nacar, Sinan; Bayram, Adem; Kankal, Murat; KANKAL, MURAT; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/İnşaat Mühendisliği Bölümü; 0000-0003-0897-4742; AAC-6221-2021
The impacts of climate change on current and future water resources are important to study local scale. This study aims to investigate the prediction performances of daily precipitation using five regression-based statistical downscaling models (RBSDMs), for the first time, and the ERA-5 reanalysis dataset in the Susurluk Basin with mountain and semi-arid climates for 1979-2018. In addition, comparisons were also performed with an artificial neural network (ANN). Before achieving the aim, the effects of atmospheric variables, grid resolution, and long-distance grid on precipitation prediction were holistically investigated for the first time. Kling-Gupta efficiency was modified and used for holistic evaluation of statistical moments parameters at precipitation prediction comparison. The standard triangular diagram, quite new in the literature, was also modified and used for graphical evaluation. The results of the study revealed that near grids were more effective on precipitation than single or far grids, and 1.50 degrees x 1.50 degrees resolution showed similar performance to 0.25 degrees x 0.25 degrees resolution. When the polynomial multivariate adaptive regression splines model, which performed slightly higher than ANN, tended to capture skewness and standard deviation values of precipitations and to hit wet/dry occurrence than the other models, all models were quite well able to predict the mean value of precipitations. Therefore, RBSDMs can be used in different basins instead of black-box models. RBSDMs can also be established for mean precipitation values without dry/wet classification in the basin. A certain success was observed in the models; however, it was justified that bias correction was required to capture extreme values in the basin.
Estimates of energy consumption in Turkey using neural networks with the teaching-learning-based optimization algorithm
(Pergamon-Elsevier, 2014-10-01) Uzlu, Ergun; Kankal, Murat; Dede, Tayfun; Akpınar, Adem; Uludağ Üniversitesi/Mühendislik Fakültesi/İnşaat Mühendisliği Bölümü.; 0000-0002-9042-6851; AAC-6763-2019; 23026855400
The main objective of the present study was to apply the ANN (artificial neural network) model with the TLBO (teaching-learning-based optimization) algorithm to estimate energy consumption in Turkey. Gross domestic product, population, import, and export data were selected as independent variables in the model. Performances of the ANN-TLBO model and the classical back propagation-trained ANN model (ANN-BP (teaching learning-based optimization) model) were compared by using various error criteria to evaluate the model accuracy. Errors of the training and testing datasets showed that the ANN-TLBO model better predicted the energy consumption compared to the ANN-BP model. After determining the best configuration for the ANN-TLBO model, the energy consumption values for Turkey were predicted under three scenarios. The forecasted results were compared between scenarios and with projections by the MENR (Ministry of Energy and Natural Resources). Compared to the MENR projections, all of the analyzed scenarios gave lower estimates of energy consumption and predicted that Turkey's energy consumption would vary between 142.7 and 158.0 Mtoe (million tons of oil equivalent) in 2020.
Estimates of hydroelectric generation using neural networks with the artificial bee colony algorithm for Turkey
(Pergamon-Elsevier, 2014-05-01) Uzlu, Ergun; Öztürk, Hasan; Nacar, Sinan; Kankal, Murat; Akpınar, Adem; Uludağ Üniversitesi/Mühendislik Fakültesi/İnşaat Mühendisliği Bölümü.; 0000-0002-9042-6851; AAC-6763-2019; 23026855400
The primary objective of this study was to apply the ANN (artificial neural network) model with the ABC (artificial bee colony) algorithm to estimate annual hydraulic energy production of Turkey. GEED (gross electricity energy demand), population, AYT (average yearly temperature), and energy consumption were selected as independent variables in the model. The first part of the study compared ANN-ABC model performance with results of classical ANN models trained with the BP (back propagation) algorithm. Mean square and relative error were applied to evaluate model accuracy. The test set errors emphasized positive differences between the ANN-ABC and classical ANN models. After determining optimal configurations, three different scenarios were developed to predict future hydropower generation values for Turkey. Results showed the ANN-ABC method predicted hydroelectric generation better than the classical ANN trained with the BP algorithm. Furthermore, results indicated future hydroelectric generation in Turkey will range from 69.1 to 76.5 TWh in 2021, and the total annual electricity demand represented by hydropower supply rates will range from 14.8% to 18.0%. However, according to Vision 2023 agenda goals, the country plans to produce 30% of its electricity demand from renewable energy sources by 2023, and use 20% less energy than in 2010. This percentage renewable energy provision cannot be accomplished unless changes in energy policy and investments are not addressed and implemented. In order to achieve this goal, the Turkish government must reconsider and raise its own investments in hydropower, wind, solar, and geothermal energy, particularly hydropower.
Evaluation of the suitability of ncep/ncar, era-interim and, era5 reanalysis data sets for statistical downscaling in the eastern black sea basin, Turkey
(Springer Wien, 2022-04-01) Nacar, Sinan; Okkan, Umut; Kankal, Murat; KANKAL, MURAT; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/İnşaat Mühendisliği Bölümü.; 0000-0003-0897-4742; AAZ-6851-2020
Climate community frequently uses gridded reanalysis data sets in their climate change impact studies. However, these studies for a region yield more realistic results depending on the rigorous analysis of the reanalysis data sets for this region. This study aims to determine the most suitable reanalysis data set for the statistical downscaling method in the Eastern Black Sea Basin, one of Turkey's most important hydrological basins owing to the precipitation it receives throughout the year. For this purpose, the monthly mean temperature and total precipitation data measured from the 12 meteorological stations and 12 large-scale predictors of the NCEP/NCAR, ERA-Interim, and ERA5 reanalysis data sets were used. The multivariate adaptive regression splines (MARS) and conventional regression analysis with linear and exponential functions were used to create effective statistical downscaling models. For evaluating and comparing the performance of the downscaling models with three different reanalysis data set, four performance statistics (root means square error, scatter index, mean absolute error, and the Nash Sutcliffe coefficient of efficiency) were used. Besides, the relative importance of the input variables of the models was determined. The study revealed that the values obtained from the models of ERA5 were closer to the precipitation and temperature values measured from the meteorological stations. In addition, the model performances with three reanalysis data sets for the temperature variable were very close to each other. The study results have shown that the MARS method, which gives the highest performance values, can be used successfully as a statistical downscaling method in climate change impact studies.
Geo-spatial multi-criteria evaluation of wave energy exploitation in a semi-enclosed sea
(Elsevier, 2021-01-01) San, Murat; Akpınar, Adem; Bingölbalı, Bilal; Kankal, Murat; KANKAL, MURAT; 0000-0003-0897-4742; AAZ-6851-2020
The present study aims to determine priority areas for installation of wave energy converters (WECs) in a semi-enclosed sea using a multi-criteria, spatial, decision-making analysis based on geographical information systems (GIS). The study also suggests a new methodology for determination of suitable areas for WECs taking into consideration different extreme wave conditions, intra-annual variation of wave conditions, and operational range of wave conditions by the WECs. A case study over a distance of 1140 km along the coast in the southwest Black Sea is presented. In the multi- criteria analysis, areas with environmental, economic, technical and social constraints are excluded. Ocean depth, distance to ports, shore, power line, and sub-station, wave climate, and sea-floor geology are all evaluated for their impact on the system implementation and weighted according to their relevance. Thus, the final suitability index (SI) map is produced and spatial statistical significance of the suitable areas is checked using hotspot analysis. Based on this, Kirklareli coastal area and the area between Igneada Cape and Kiyikoy village are determined as primary priority areas. The sustainability parameters with different weights proposed in this study do not differentiate priority areas but affect the SI scores. (C) 2020 Elsevier Ltd. All rights reserved.
Increasing trends in spectral peak energy and period in a semi-closed sea
(Pergamon-elsevier Science Ltd, 2023-02-13) Acar, Emine; Akpınar, Adem; AKPINAR, ADEM; Kankal, Murat; KANKAL, MURAT; Amarouche, Khalid; AMAROUCHE, KHALID; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/İnşaat Mühendisliği Bölümü.; 0000-0002-9042-6851; 0000-0003-0897-4742; 0000-0001-7983-4611; JTU-9268-2023; AAC-6763-2019; AAZ-6851-2020; AFR-7886-2022
This study aims to investigate long-term trends in the Black Sea's spectral wave peak energy and periods. Improved Visualization of the Innovative Trend Analysis and the Mann-Kendal methods was applied to the maximum and mean spectral peak energies and peak periods between 1979 and 2020. Long-term spectral data are obtained from the ERA5 reanalysis and two spectral wave models, SWAN and WWIII. The innovative trend analysis method has the particularity to examine trends in higher and lower value categories. Studies of long-term changes in spectral wave characteristics are rare, and trends in spectral peak parameters are evaluated in this study for the first time in the Black Sea. It was detected that both spectral peak energies and peak periods tend to increase predominantly over most of the time scales. Furthermore, while the change rates for peak en-ergies do not exceed 40% annually and seasonally, change rates exceeding 100% are observed on a monthly basis. Besides, the change rates of the peak periods vary in the +/- 5% band and usually do not exceed 15%. Moreover, despite a few differences, trend analysis results obtained using SWAN and WWIII models were close to the global ERA5 results. The results may provide insight into the design and durable development of coastal and marine structures as well as the evaluation of wave climate change based on spectral wave data.
Innovative and polygonal trend analyses applications for rainfall data in Vietnam
(Springer, 2021-02-16) Şan, Murat; Linh, Nguyen Thi Thuy; Pham, Quoc Bao; Kankal, Murat; Akçay, Fatma; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/İnşaat Mühendisliği Bölümü.; 0000-0003-0897-4742; AAZ-6851-2020; CBG-3616-2022; 24471611900; 57222226657
It is a known fact that the size, frequency, and spatial variability of hydrometeorological variables will irregularly increase under the impact of climate change. Among the hydrometeorological variables, rainfall is one of the most important. Trend analysis is one of the most effective methods of observing the effects of climate change on rainfall. Recently, new graphical methods have been proposed as an alternative to classical trend analysis methods. Innovative Polygon Trend Analysis (IPTA), which evolved from Innovative Trend Analysis (ITA), is currently one of the proposed methods and it does not contain any assumptions. The aim of this study is to compare IPTA, ITA with the Significance Test and Mann-Kendall (MK) methods. To achieve this, the monthly total rainfall trends of 15 stations in the Vu Gia-Thu Bon River Basin (VGTBRB) of Vietnam have been examined for the period 1979-2016. The analyses show that rainfall tends to increase (decrease) in March (June) at nearly all stations. IPTA and ITA with the Significance Test are more sensitive than MK in determining the trends. While trends were detected in approximately 90% of all months in IPTA and ITA with the Significance Test, this rate was only 23% in the MK test. Although the arithmetic mean graphs in the 1-year hydrometeorological cycle are considerably regular at almost all stations, their standard deviations are relatively irregular. The most critical month for trend transitions between consecutive months for all the stations is October, which has an average trend slope of -1.35 and a trend slope ranging from -3.98 to -0.21, which shows a decreasing trend.
Innovative approaches to the trend assessment of streamflows in the Eastern Black Sea basin, Turkey
(Taylor & Francis Ltd, 2022-01-20) San, Murat; Akçay, Fatma; Kankal, Murat; KANKAL, MURAT; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/İnşaat Mühendisliği Bölümü.; 0000-0003-0897-4742; AAZ-6851-2020
The issue of detection of hydrometeorological trends remains relevant because of the importance of climate change in design, operation, and management studies related to water resources. This study examines the effects of changes in climate and land use on monthly flows (1962-2018) in the Eastern Black Sea basin, Turkey, using innovative trend analysis methods. In this context, innovative polygon trend analysis (IPTA) and innovative trend significance test (ITST) were used to detect the trends and compared with Mann-Kendall test. Only stations with homogeneous data that did not experience non-climatic changes are used in the analysis. IPTA and ITST approaches are much more sensitive than Mann-Kendall in detecting trends. Although the innovative methods are mostly compatible with each other (90%), IPTA presents additional information about trend transitions between successive parts of time series. Results indicate significant decreasing trends in summer months, likely due to diminishing precipitation and effective evaporation.
Innovative polygon trend analyses with star graph for rainfall and temperature data in agricultural regions of Turkey
(Springer, 2022-12-01) Şan, Murat; Acar, Emine; Kankal, Murat; KANKAL, MURAT; Akçay, Fatma; ; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/İnşaat Mühendisliği Bölümü.; 0000-0003-0897-4742; JTU-9268-2023; AAZ-6851-2020
Agriculture is affected by climate change, such as extreme increases or decreases rainfall and temperature patterns. It is possible to research that effect by using trend analysis methodologies. This paper investigates trends of monthly total rainfall and mean temperature data of nine selected stations from agricultural regions of Turkey between 1969 and 2020. To the end, the classical Mann-Kendall, Innovative Trend Significance Test (ITST), and Innovative Polygon Trend Analysis (IPTA) with Star Graph methods providing the opportunity to examine seasonal behavior were used for trend analysis. The analysis reveals that about 96% of all monthly rainfall in the Mann-Kendall test has no trend. However, nearly all stations tend to decrease (increase) in November (September) in both innovative approaches. For temperature, it is seen that increasing trend or no trend dominated in general. There were increasing trends in the innovative approaches throughout the year except for April. Temperatures have increased significantly throughout the year in all regions over the last decades. With the help of IPTA, it was also concluded that the seasonal internal variability of rainfall over the entire time and in the last 30 years is quite complex and persists in all agricultural regions. The results show that irregular changes in rainfall and rising temperatures in all stations negatively affected crop yield and/or required more irrigation. In addition, according to the results obtained by comparing trend methods, innovative approaches are very insistent on determining of trend and provide additional information through a visual review of trend behaviors.
Investigation of the effect of building-based assessment on flood hazard evaluation
(Hard, 2022-01-01) Kurt, Zeynep O.; Yüksek, Ömer; Kankal, Murat; KANKAL, MURAT; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/İnşaat Mühendisliği Bölümü.; 0000-0003-0897-4742; AAZ-6851-2020
The aim of this study is to investigate the effect of building-based assessment on flood hazards. Degirmendere, which is one of the most important basins of the Eastern Black Sea Region of Turkey, was selected as the study area. Flood hazards for discharge values with different return periods of this region were found using; the damage percentages table recommended by the Huntington Civil Engineers Association (HCEA), the Van Eck and Kok depth-loss curves, and the equation obtained by Pistrika and Jonkman. The calculations were performed both for building-based and for the region-based and compared. Furthermore, the damage rates obtained for each building on a building-based were graded and shown on the map. It was determined that the damage on individual building-based estimation for Q1,000 calculated more than to the one on region-based 154% and 17% for the damage percentages table recommended by the HCEA, Van Eck and Kok depth-loss curves, respectively. Otherwise, it gave 11% less damage estimation for the equation obtained by Pistrika and Jonkman. Furthermore, it was concluded that the increase in damage from Q(50) to Q(100), from Q(100) to Q(500) , and from Q(500) to Q(1.000) was between 16%-30% according to building-based damage estimation.
Karadeniz'de spektral pik dalga enerjisinin eğilim analizi
(Bursa Uludağ Üniversitesi, 2022-09-02) Acar, Emine; Kankal, Murat; Bursa Uludağ Üniversitesi/Fen Bilimleri Enstitüsü/İnşaat Mühendisliği Anabilim Dalı.
Bu çalışmada, Karadeniz’de dalga koşullarının analizi gerçekleştirilerek dalgaların sahip oldukları spektral pik enerji ve pik periyodun uzun dönemli yıllık, mevsimlik ve aylık ortalama ve maksimum değerlerinin eğilimlerinin araştırılması hedeflenmiştir. Bu bağlamda, eğilim analizinin uygulanacağı veri seti olarak SWAN üçüncü nesil dalga tahmin modeli çıktıları ve ERA5 yeniden analizine ait 1979-2020 yıllarını kapsayan dalga spektrumları kullanılmıştır. SWAN dalga modelinin çıktılarından ve ERA5 yeniden analizi veri setinden Karadeniz üzerinde seçilen sırası ile 36 ve 876 istasyonda veriler çekilmiş ve işlenmiştir. 42 yıllık periyotta veri setlerinden çekilen dalga spektrumları işlenerek saatlik spektral pik enerji ve pik periyodu verileri ayıklanmıştır. Daha sonra, bu parametrelerin yıllık, mevsimlik ve aylık zaman ölçeklerinde ortalama ve maksimum değerleri hesaplanmıştır. En son adım olarak, üç farklı zaman ölçeğinde dört parametreye Mann-Kendall ve Geliştirilmiş Görselleştirme ile Yenilikçi Eğilim Analizi testleri uygulanmıştır. Böylece, Mann-Kendall ile bütüncül bir şekilde ve yenilikçi yöntem ile alt kategorilerde eğilim sonuçları irdelenmiş, bunun yanı sıra bölgesel ve küresel veri setlerinin eğilim analizi sonuçlarının kıyasının yapılabilmesi mümkün olmuştur. Sonuçta, spektral pik enerjinin her iki model sonucunda da Karadeniz genelinde ilkbahar ve yaz mevsimleri ile Ocak, Mart, Nisan, Ağustos ve Eylül aylarında artan, kış ve sonbahar mevsimleri ile Mayıs, Temmuz, Kasım ve Aralık aylarında ise azalan eğilimin baskın olduğu tespit edilmiştir. Ayrıca, pik periyotlar neredeyse tüm zaman ölçeklerinde artan eğilim, sonbahar mevsimi ile Mayıs ve Kasım aylarında ise azalan eğilim göstermiştir. SWAN dalga tahmin modeli ve ERA5 yeniden analiz verilerinden elde edilen sonuçların her iki parametre için de birbirini benzer olduğu görülmüştür. Ancak, pik periyotların yıllık ölçekte, kış ve sonbahar aylarında ERA5 ile artan eğilim gösterdiği bölgelerde SWAN modeli ile azalan eğilim tespit edilmiştir. Mann-Kendall testinde güven seviyesi ve yenilikçi yöntemde değişim oranı dikkate alındığında artan eğilimlerin azalan eğilimlere kıyasla daha şiddetli olduğu görülmüştür.
Performance evaluation of multiple adaptive regression splines, teaching–learning based optimization and conventional regression techniques in predicting mechanical properties of impregnated wood
(Springer, 2019-07) Tiryaki, Sebahattin; Tan, Hüseyin; Bardak, Selahattin; Nacar, Sinan; Peker, Hüseyin; Kankal, Murat; Uludağ Üniversitesi/Mühendislik Fakültesi/İnşaat Mühendisliği Bölümü.; 0000-0003-0897-4742; AAZ-6851-2020; 24471611900
Understanding the mechanical behaviour of impregnated wood is crucial in making a preliminary decision on the usability of such woods for structural purposes. In this paper, by considering concentration (1, 3 and 5%), pressure (1, 1.5 and 2atm.), and time (30, 60, 90 and 120min), an experimental study was performed, and the mechanical behaviour of impregnated wood was determined as a result of the experimental process. Multiple adaptive regression splines (MARS), teaching-learning based optimization (TLBO) algorithms and conventional regression analysis (CRA) were applied to different regression functions by using experimentally obtained data. The functions were checked against each other to detect the best equation for each parameter and to assess performances of MARS, TLBO and CRA methods in the prediction of mechanical properties. The experimental results showed that higher values of mechanical properties were obtained when lower concentration, pressure and time were chosen. Overall, all the functions successfully predicted the mechanical properties. However, the MARS and TLBO provided better accuracy in predicting the mechanical properties. The modeling results indicated that the MARS and TLBO are promising new methods in predicting the mechanical properties of impregnated wood. With the use of these methods, the mechanical behavior of impregnated wood could be determined with high levels of accuracy. Thus, the proposed methods may facilitate a preliminary decision concerning the usability of such woods for areas where the mechanical properties are important. Finally, the employment of MARS and TLBO algorithms by practitioners in the wood industry is encouraged and recommended for future studies.
Prediction of maximum annual flood discharges using artificial neural network approaches
(Croatian Society of Civil Engineers, 2020-04-10) Anılan, Tuğçe; Nacar, Sinan; Yüksek, Ömer; Kankal, Murat; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/İnşaat Mühendisliği Bölümü.; 0000-0003-0897-4742; AAZ-6851-2020; 24471611900
The applicability of artificial neural network (ANN) approaches for estimation of maximum annual flows is investigated in the paper. The performance of three neural network models is compared: multi layer perceptron neural networks (MLP_NN), generalized feed forward neural networks (GFF_NN), and principal component analysis with neural networks (PCA_ NN). The proposed approaches were applied to 33 stream-gauging stations. It was found that the optimal 3-hidden layered PCA_NN method was more appropriate than the optimal MLP_NN and GFF_NN models for the estimation of maximum annual flows.
Prediction of parameters which affect beach nourishment performance using MARS, TLBO, and conventional regression techniques
(Springer, 2019-08-09) Karasu, Servet; Nacar, Sinan; Uzlu, Ergun; Yüksek, Ömer; Kankal, Murat; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/İnşaat Mühendisliği.; 0000-0003-0897-4742; AAZ-6851-2020; 24471611900
Artificial beach nourishment is one of the most important environmentally friendly coastal protection methods since it protects the aesthetic and recreational values of the beach and increases its protective properties. Therefore, the main aim of the current study is to assess the accuracy of multivariate adaptive regression splines (MARS) in predicting the parameters, namely sediment transport coefficients (K) and the diffusion rate (omega), which affect beach nourishment performance. The performance of the MARS was determined by comparison of the models using exponential, linear, and power regression equations trained by conventional regression analyses (CRA) and the teaching-learning based optimization (TLBO) algorithm. In all models, two different input data obtained from the experimental study were used, one dimensional and one non-dimensional. The results presented that the MARS models gave lower error values than the CRA and TLBO models according to the root mean square error, mean absolute error, and scattering index criteria. When the models were evaluated, it was revealed that dimensional and non-dimensional models gave approximate results. We proved that the dimensional and non-dimensional MARS models can be used to estimate the (K) and (omega) values.
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.
Reply to discussion of "innovative approaches to the trend assessment of streamflows in the eastern Black Sea basin, Turkey"
(Taylor & Francis Ltd, 2023-03-31) Akçay, Fatma; Kankal, Murat; Şan, Murat; Akçay, Fatma; KANKAL, MURAT; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/İnşaat Mühendisliği Bölümü.; 000-0001-8129-3009; 0000-0003-0897-4742; CBG-3616-2022; AAZ-6851-2020
In this reply we thank the authors for their comments on our article "Innovative approaches to trend assessment of streamflows in the Eastern Black Sea basin, Turkey." They stated that the trend slope calculations in our study are incorrect. A response to the discussion on this topic offered.
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.