Sürdürülebilir Kalkınma Amaçları
Yazarlar
- Celik, Erkan(3)
- Gul, Muhammet(2)
- Yucesan, Melih(2)
- Acar, Ramazan(1)
- Ak, Kübra(1)
- Alparslan, Cem(1)
- Arici, Erdinc(1)
- Aydın, Baki(1)
- Aytac, Ayca(1)
- Er, Akif(1)
- Félix, Luís M.(1)
- Hoseini, Seyyed Morteza(1)
- Işık, Erdem(1)
- Kurtul, Irmak(1)
- Köse, Özay(1)
- Minaz, Mert(1)
- Serdar, Osman(1)
- Tuna, M. Cihat(1)
- Tunc, Mustafa(1)
- İnallı, Mustafa(1)
Yayınlar
- MakaleAçık Erişim2026Scopus SJRQ1Optimization of scours downstream of conduit aerators
Arici, Erdinc; Tuna, M. Cihat; Aytac, Ayca; Acar, Ramazan; Tunc, Mustafa
Dams, wastewater treatment plants, and aquaculture areas often require aeration, making it crucial for hydraulic engineers to predict the physical properties of scour holes occurring on the downstream side of conduits. Conduit systems that provide high aeration performance necessitate multiple optimization studies to simultaneously maximize the scour parameters and the system’s physical properties. In this study, the physical parameters of conduit systems and downstream scour parameters were optimized using Artificial Neural Networks (ANN). The optimum system parameters were identified based on the experimental results through ANN-based optimization. Additionally, the real and optimal effects of the parameters on aeration performance and scour were determined. The results of the study demonstrated that ANN can be effectively used in studies aimed at optimizing the aeration performance and scour values of conduits. This study is the first to apply ANN for the simultaneous optimization of both aeration efficiency (Qa/Qw) and scour characteristics (dmax and δ) in a conduit-based hydraulic system. Previous studies have typically focused on either scour or aeration individually; this study integrates both through a multi-objective ANN model. The findings provide novel insights for the design and retrofitting of energy-dissipating conduit structures in high-head dam applications. Unlike traditional empirical or regression-based approaches, the ANN model captures complex, nonlinear relationships among multiple interacting hydraulic variables. It identified optimal parameter combinations — such as 70% gate opening, 1.30–1.50 m conduit length, and 9 mm air hole diameter — that improve both aeration and scour behavior. These outcomes offer direct applicability for real-world hydraulic system optimization.
AtıfOpenAlex1
İncele - MakaleYalnız künye2026Scopus SJRQ1Evaluation of rainbow trout response to the optimal anesthetic concentration of citronella oil (Cymbopogon nardus) predicted by artificial neural network
Minaz, Mert; Félix, Luís M.; Er, Akif; Köse, Özay; Ak, Kübra; Alparslan, Cem; Serdar, Osman; Aydın, Baki; Kurtul, Irmak; Hoseini, Seyyed Morteza
Anesthesia practices in aquaculture are important for preventing potential stress in fish. For this purpose, interest in natural anesthetic agents as substitute for currently used synthetic anesthetics has increased. In this context, the present study demonstrated the anesthetic efficacy of citronella oil (Cymbopogon nardus) in rainbow trout (Oncorhynchus mykiss) individuals. Based on an artificial neural network (ANN) model derived from preliminary studies, the optimal anesthetic concentration for rainbow trout individuals was estimated as 90 μL L−1. Validation of the concentration predicted by ANN revealed no time-dependent hematological or histological effects on fish to citronella oil. Furthermore, biochemical tests revealed that the partial post-anesthesia increase in ALT and AST values was transient and negligible in light of histological findings. Although oxidative stress was observed during the acute period, the enzymatic activity of SOD and CAT effectively reduced the oxidative damage, demonstrating their role in protecting the fish from oxidative stress. The antioxidant activity observed was temporary, returning to baseline levels without any accompanying tissue damage at the tested concentration. Behavioral monitoring further indicated a swift reduction in motor activity followed by a smooth recovery process, with no evident signs of stress or avoidance behaviour. Overall, these results position citronella oil as a promising natural anesthetic for rainbow trout under the studied parameters. That said, further studies involving various solvents, temperature ranges, and fish species are necessary to comprehensively establish its efficacy and safety.
AtıfScopus4İncele - MakaleYalnız künye2020Scopus SJRQ2A multi-method patient arrival forecasting outline for hospital emergency departments
Yucesan, Melih; Gul, Muhammet; Celik, Erkan
Patient arrivals at the emergency department (ED) of hospitals has an unpredictable behavior. So that, adequate forecasting of this process can serve a management baseline to better allocate ED human resources and medical equipment. In this paper, a multi-method patient arrival forecasting outline for EDs is developed. The methods followed within this study include single methods as linear regression (LR), autoregressive integrated moving average (ARIMA), artificial neural network (ANN), exponential smoothing and hybrid methods as ARIMA–ANN and ARIMA-LR. As the subject of the study, a private hospital ED case in Turkey is carried out. Data of ED patient arrivals for the year of 2016 was used to set up models. Forecasting performance of the multi-method outline was measured using mean absolute percentage error. The ARIMA–ANN hybrid model is shown to outperform in terms of forecasting accuracy. In order to contribute to the current knowledge, this paper is a novel attempt of applying these methods to model ED patient arrivals and making an overall assessment among them. The results can be used to aid in strategic decision-making on ED staffing and scheduling policy planning in response to predictable arrival variations.
AtıfScopus29WoS26OpenAlex37İncele - Derleme MakaleAçık Erişim2019Scopus SJRQ2ANN and ANFIS Approaches to Calculate the Heating and Cooling Degree Day Values: The Case of Provinces in Turkey
Işık, Erdem; İnallı, Mustafa; Celik, Erkan
Energy analysis plays a vital role in developing an optimum and cost-effective design for the heating, ventilating and air conditioning system of a building. The values of heating degree days (HDD) and cooling degree days (CDD) are used to analyze the demand for energy for buildings. In this study, the HDD and CDD values for Turkey were calculated using estimated temperature values. An artificial neural network (ANN) and an adaptive network-based fuzzy inference system (ANFIS) were applied for the estimation of temperature values. The Levenberg–Marquardt training algorithm was used for the feedforward backpropagation of the ANN, whereas the Sugeno-type fuzzy inference system was used for the ANFIS. Meteorological data from the last 11 years were used for the proposed approaches, and these were applied to different stations (n = 50) of the Turkish State Meteorological Service. In relation to the insulated building design, base temperatures have been considered as 16 °C and 18 °C for the HDD and 22 °C and 24 °C for the CDD based on a literature review. Two novel maps were prepared for the CDD and HDD. The calculated degree days and the maps are consistent with those from the Turkish State Meteorological Service.
AtıfScopus13WoS13OpenAlex18İncele - MakaleAçık Erişim2018Scopus SJRQ3Performance comparison between ARIMAX, ANN and ARIMAX-ANN hybridization in sales forecasting for furniture industry; [Usporedba performansi modela ARIMAX, ANN i hibridizacije ARIMAX-ANN u predviđanju prodaje za industriju namještaja]
Yucesan, Melih; Gul, Muhammet; Celik, Erkan
Manufacturing firms aim to increase their profits and reduce costs in a competitive and rapidly changing market. One of the most important ways to reach these goals is to forecast sales correctly. Furniture manufacturing, which is considered a prosperous and growing industry in Turkey, has an increasing trend related to the growth in construction and associated industries, increase in urban migration and increase in per capita income. Accuracy of sales forecasting in furniture industry is affected by external factors, such as consumer confidence index, producer price index, month of the year and number of vacation days as well as the time factor itself. This study aims to develop an Autoregressive Integrated Moving Average with external variables (ARIMAX) to forecast the total monthly sales of furniture products of a well-known manufacturer in Turkey. As a follow up study, a performance comparison between ARIMAX, artificial neural networks (ANNs) and ARIMAX-ANN hybridization is performed. In conclusion, results of performance measures demonstrate that hybrid model developed for each amount of product sales give better accuracy values than single methods. Overall, it is proved that using the ARI-MAX and hybridization of this method with ANN are applicable for forecasting monthly sales of furniture products.
AtıfScopus7WoS5OpenAlex20İncele