Person: KANKAL, MURAT
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KANKAL
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MURAT
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Publication 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-6851-2020The 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.Publication 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-6851-2020The 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.Publication 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-6851-2020Accurate 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.Publication 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-6851-2020Agriculture 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.Publication 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-6851-2020In 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.Publication Wave power trends over the mediterranean sea based on innovative methods and 60-year ERA5 reanalysis(Mdpi, 2023-05-22) Acar, Emine; Akpınar, Adem; Kankal, Murat; Amarouche, Khalid; Acar, Emine; AKPINAR, ADEM; KANKAL, MURAT; AMAROUCHE, KHALID; Bursa Uludağ Üniversitesi/Mühendislik Fakültesi/İnşaat Mühendisliği Bölümü.; 0000-0002-9042-6851; 0000-0003-0897-4742; 0000-0001-7983-4611; AAZ-6851-2020; AAC-6763-2019; ABG-2101-2020; JTU-9268-2023; AFR-7886-2022The present study aims to evaluate long-term wave power (P-wave) trends over the Mediterranean Sea using innovative and classical trend analysis techniques, considering the annual and seasonal means. For this purpose, the data were selected for the ERA5 reanalysis with 0.5 degrees x 0.5 degrees spatial resolution and 1 h temporal resolution during 60 years between 1962 and 2021. Spatial assessment of the annual and seasonal trends was first performed using the innovative trend analysis (ITA) and Mann-Kendall (MK) test. To obtain more detailed information, innovative polygon trend analysis (IPTA), improved visualization of innovative trend analysis (IV-ITA), and star graph methods were applied to annual, seasonal, and monthly mean Pwave at 12 stations selected. The results allow us to identify an increasing trend above the 10% change rate with the innovative method and above the 95% confidence level with the Mann-Kendall test in mean wave power in the Levantine basin and the Libyan Sea at all timescales. The use of various innovative methods offered similar results in certain respects and complemented each other.