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- MakaleYalnız künye2023Scopus SJRQ1A long short-term memory-based hybrid model optimized using a genetic algorithm for particulate matter 2.5 prediction
Utku, Anıl; Can, Ümit; Kamal, Mustafa; Das, Narasingha; Cifuentes-Faura, Javier; Barut, Abdulkadir
Beijing, Shanghai, Singapore, and London are regions with high population density and industrial activities. In this sense, accurate prediction of the rate of particulate matter 2.5 (PM_2.5), one of the most important air pollutants, is very important for creating a healthy ecological system. This study proposes a hybrid method to predict PM_2.5 with high success. This method aims to optimize the Long Short-Term Memory (LSTM) deep learning model, which successfully solves time series problems with a Genetic Algorithm (GA). Here, the hyperparameters of the LSTM model, such as the number of layers, number of neurons, number of epochs, and kernel size, are optimized to achieve the highest prediction rate. Against this backdrop, the present study uses for the first time a hybrid GA-LSTM model to predict PM_2.5 concentrations in Beijing, Shanghai, Singapore, and London. The Beijing dataset consists of 13 attributes, the Shanghai dataset consists of 10 attributes, the London dataset consists of 5 attributes, and the Singapore dataset consists of 7 attributes. In this study, input variables such as PM_2.5, DEWP, TEMP, PRESS, WS, WD, PRE, Snowfall hours, Rain hours, and Season were used. To assess the effectiveness of this hybrid method, the developed GA-LSTM model was compared with k-Nearest Neighbors (kNN), Random Forest (RF), Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), Convolutional Neural Networks (CNN), LSTM, and Gated Recurrent Units (GRU). Experimental results showed that GA-LSTM outperformed the compared models. In addition, one of the best results was obtained compared to other models in the literature.
AtıfScopus17OpenAlex18
İncele - MakaleAçık Erişim2021Scopus SJRQ4DETERMINATION OF THE ANTIOXIDANT PARAMETERS OF COMMONLY AVAILABLE BLACK PINE (PINUS NIGRA J.F. ARNOLD)
Yildirim, Nuran Cikcikoglu; Gezer, Esref; Yildirim, Numan
The antioxidant parameters of black pine (Pinus nigra J.F. Arnold) needles samples collected from different areas in the province of Diyarbakir, Turkey were studied. Black pine needles samples were collected from three different locations (Egil, Kosuyolu, and Diyarbakir industrial area) in the province of Diyarbakir in summer and winter seasons. The activity of enzymatic antioxidants such as superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GPX), glutathione S-transferase (GST) and malondialdehyde (MDA) levels of the samples were determined by ELISA method. Results showed that SOD enzyme activities of the samples collected in summer from Kosuyolu and the industrial area decreased compared to the control group, but increased in the samples collected in winter from the same areas. When compared to the control group, it was observed that the MDA levels of the samples collected from Kosuyolu and the industrial area increased.