6 results
Search Results
2. Prediction of compressive and tensile strength of Gaziantep basalts via neural networks and gene expression programming.
- Author
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Çanakcı, Hanifi, Baykasoğlu, Adil, and Güllü, Hamza
- Subjects
SOFT computing ,ARTIFICIAL neural networks ,GENE expression ,BASALT ,STRENGTH of materials ,REGRESSION analysis - Abstract
In this paper, two soft computing approaches, which are known as artificial neural networks and Gene Expression Programming (GEP) are used in strength prediction of basalts which are collected from Gaziantep region in Turkey. The collected basalts samples are tested in the geotechnical engineering laboratory of the University of Gaziantep. The parameters, “ultrasound pulse velocity”, “water absorption”, “dry density”, “saturated density”, and “bulk density” which are experimentally determined based on the procedures given in ISRM (Rock characterisation testing and monitoring. Pergamon Press, Oxford, 1981) are used to predict “uniaxial compressive strength” and “tensile strength” of Gaziantep basalts. It is found out that neural networks are quite effective in comparison to GEP and classical regression analyses in predicting the strength of the basalts. The results obtained are also useful in characterizing the Gaziantep basalts for practical applications. [ABSTRACT FROM AUTHOR]
- Published
- 2009
- Full Text
- View/download PDF
3. Structural recurrent neural network models for earthquake prediction.
- Author
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Doğan, Aydın and Demir, Engin
- Subjects
RECURRENT neural networks ,ARTIFICIAL neural networks ,DEEP learning ,EARTHQUAKE zones ,EARTHQUAKES ,EARTHQUAKE prediction ,PREDICTION models - Abstract
The earthquake prediction problem can be defined as given a minimum Richter magnitude scale and a specified geographic region, predicting the possibility of an earthquake in that region within a time interval. This is a long-time studied research problem but not much progress is achieved until the last decade. With the advancement of computational systems and deep learning models, significant results are achieved. In this study, we introduce novel models using the structural recurrent neural network (SRNN) that capture the spatial proximity and structural properties such as the existence of faults in regions. Experimental results are carried out using two distinct regions such as Turkey and China where the scale and earthquake zones differ greatly. SRNN models achieve better performance results compared with the baseline and the state-of-the-art models. Especially the SRNNClass near model, that captures the first-order spatial neighborhood and structural classification based on fault lines, results in the highest F 1 score. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
4. Estimates of greenhouse gas emission in Turkey with grey wolf optimizer algorithm-optimized artificial neural networks.
- Author
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Uzlu, Ergun
- Subjects
ARTIFICIAL neural networks ,GREENHOUSE gases ,BEES algorithm ,RENEWABLE energy sources ,ANT algorithms ,BACK propagation ,GROSS domestic product - Abstract
The main purpose of this study was to predict Turkey's future greenhouse gas (GHG) emissions using an artificial neural network (ANN) model trained by a grey wolf optimizer (GWO) algorithm. Gross domestic product, energy consumption, population, urbanization rate, and renewable energy production data were used as predictor variables. To probe the accuracy of the proposed model, the new ANN-GWO model's performance was compared with the performance of ANN-BP (back propagation), ANN-ABC (artificial bee colony), and ANN-TLBO (teaching–learning-based optimization) models using multiple error criteria. According to calculated error values, the ANN-GWO models predicted GHG emissions more accurately than classical ANN-BP, ANN-ABC, and ANN-TLBO models. According to the average relative error values calculated for the test set, ANN-GWO performs 32.23% better than ANN-BP, 35.29% better than ANN-ABC, and 19.33% better than ANN-TLBO. Using the ANN-GWO model, GHG emissions were forecasted out to 2030 under three different scenarios. The predictions obtained, consistent with a prior forecasting study in the literature, indicated that GHG emissions are expected to outpace official predictions (model prediction range for 2030, 956.97–1170.54 Mt CO
2 equivalent). The present study demonstrated that GHG emissions can be predicted accurately with an ANN-GWO model, and that the GWO optimization method is advantageous for predicting future GHG emissions. [ABSTRACT FROM AUTHOR]- Published
- 2021
- Full Text
- View/download PDF
5. Forecasting of Turkey's monthly electricity demand by seasonal artificial neural network.
- Author
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Hamzaçebi, Coşkun, Es, Hüseyin Avni, and Çakmak, Recep
- Subjects
ELECTRIC power consumption ,ARTIFICIAL neural networks ,ELECTRIC utilities ,SOUTHERN oscillation ,ELECTRICITY ,ENERGY policy - Abstract
Electricity is one of the most important end-user energy types in today's world and has an effective role in development of societies and economies. Stability of electricity supply is provided by matching of generated and consumed electricity amount during the all-day. So, electricity consumption forecasting is an essential issue for electric utilities. In this study, the monthly electricity demand of Turkey has been predicted. To model the effects of seasonality and trend, four different ANN models have been developed and selected the superior one. In addition, the selected ANN model has been compared with SARIMA model in order to increase the acceptability and reliability of the ANN model. The monthly electricity demand of Turkey has been predicted between 2015 and 2018 via the ANN model that can make successful and high-accuracy predictions according to the performance measures. The forecasting values will help in determining the medium-term and stable energy policies. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
6. Forecasting highway casualties under the effect of railway development policy in Turkey using artificial neural networks.
- Author
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Doğan, Erdem and Akgüngör, Ali
- Subjects
TRAFFIC accidents ,RAILROADS ,ARTIFICIAL neural networks ,NONLINEAR analysis ,MULTIPLE regression analysis ,GROSS national product ,PER capita - Abstract
This study presents forecast of highway casualties in Turkey using nonlinear multiple regression (NLMR) and artificial neural network (ANN) approaches. Also, the effect of railway development on highway safety using ANN models was evaluated. Two separate NLMR and ANN models for forecasting the number of accidents ( A) and injuries ( I) were developed using 27 years of historical data (1980-2006). The first 23 years data were used for training, while the remaining data were utilized for testing. The model parameters include gross national product per capita (GNP-C), numbers of vehicles per thousand people (V-TP), and percentage of highways, railways, and airways usages (TSUP-H, TSUP-R, and TSUP-A, respectively). In the ANN models development, the sigmoid and linear activation functions were employed with feed-forward back propagation algorithm. The performances of the developed NLMR and ANN models were evaluated by means of error measurements including mean absolute percentage error (MAPE), mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination ( R). ANN models were used for future estimates because NLMR models produced unreasonably decreasing projections. The number of road accidents and as well as injuries was forecasted until 2020 via different possible scenarios based on (1) taking TSUPs at their current trends with no change in the national transport policy at present, and (2) shifting passenger traffic from highway to railway at given percentages but leaving airway traffic with its current trend. The model results indicate that shifting passenger traffic from the highway system to railway system resulted in a significant decrease on highway casualties in Turkey. [ABSTRACT FROM AUTHOR]
- Published
- 2013
- Full Text
- View/download PDF
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