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 CO2 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.
Similar content being viewed by others
References
Republic of Turkey Ministry of Environment and Urbanization. Turkey environmental performance review report. https://webdosya.csb.gov.tr/db/ab/icerikler/oecd-epr-tr-20190228120557.pdf
Turkish Statistical Institute (TURKSTAT). Greenhouse gas emission statistics 1990–2018. https://webdosya.csb.gov.tr/db/iklim/icerikler/turk-ye-istat-st-k-kurumu-sera-gazi-em-syon-istat-st-kler--1990-2018-20200506122539.pdf
Natural Gas Distribution Companies Association of Turkey. Carbon emission report. https://www.gazbir.org.tr/uploads/page/Karbon%20Emisyonu-Rev-Son.pdf
Choi CS, Abdullah L (2016) Prediction of carbon dioxide emissions using two linear regression-based models: a comparative analysis. J Appl Eng 4:305–312
Pao HT, Tsai CM (2011) Modeling and forecasting the CO2 emissions, energy consumption, and economic growth in Brazil. Energy 36:2450–2458
Lotfalipour MR, Falahi MA, Bastam M (2013) Prediction of CO2 emissions in Iran using grey and ARIMA models. Int J Energy Econ Policy 3:229–237
Wang ZX, Li Q (2019) Modelling the nonlinear relationship between CO2 emissions and economic growth using a PSO algorithm-based grey Verhulst model. J Clean Prod 207:214–224
Ding S, Dang YG, Li X, Wang JJ, Zhao K (2017) Forecasting Chinese CO2 emissions from fuel combustion using a novel grey multivariable model. J Clean Prod 162:1527–1538
Yu Y, Deng YR, Chen FF (2018) Impact of population aging and industrial structure on CO2 emissions and emissions trend prediction in China. Atmos Pollut Res 9:446–454
Zhao X, Han M, Ding L, Calin AC (2018) Forecasting carbon dioxide emissions based on a hybrid of mixed data sampling regression model and back propagation neural network in the USA. Environ Sci Pollut Res 25:2899–2910
Chen Z, Ye X, Huang P (2018) Estimating carbon dioxide (CO2) emissions from reservoirs using artificial neural networks. Water 10:1–16
Zhou J, Du S, Shi J, Guang F (2017) Carbon emissions scenario prediction of the thermal power industry in the Beijing-Tianjin-Hebei region based on a back propagation neural network optimized by an improved particle swarm optimization algorithm. Pol J Environ Stud 26:1895–1904
Tian L, Gao L, Xu P (2010) The evolutional prediction model of carbon emissions in china based on bp neural network. Int J Nonlinear Sci 10:131–140
Appiah K, Du J, Appah R, Quacoe D (2018) Prediction of potential carbon dioxide emissions of selected emerging economies using artificial neural network. J Environ Sci Eng 7:321–335
Radojevic D, Pocajt V, Popovic I, Grujic AP, Ristic M (2013) Forecasting of greenhouse gas emissions in Serbia using artificial neural networks. Energy Sources Part A Recovery Util Environ Eff 35:733–740
Santibanez-Gonzalez E, Del R, Robson MG, Pacca LH (2011) Solving a public sector sustainable supply chain problem: a genetic algorithm approach. In: Proc. of Int. Conf. of Artificial Intelligence (ICAI), Las Vegas, USA, pp. 507–512
Uzlu E, Akpınar A, Öztürk HT, Nacar S, Kankal M (2014) Estimates of hydroelectric generation using neural networks with the artificial bee colony algorithm for Turkey. Energy 69:638–647
Uzlu E, Kankal M, Akpınar A, Dede T (2014) Estimates of energy consumption in Turkey using neural networks with the teaching–learning-based optimization algorithm. Energy 75:295–303
Kankal M, Uzlu E (2017) Neural network approach with teaching–learning-based optimization for modeling and forecasting long-term electric energy demand in Turkey. Neural Comput Appl 28:737–747
Li W, Gao S (2018) Prospective on energy related carbon emissions peak integrating optimized intelligent algorithm with dry process technique application for China’s cement industry. Energy 165:33–54
Sangeetha A, Amudha T (2018) A novel bio-inspired framework for CO2 emission forecast in India. Procedia Comput Sci 125:367–375
Hong T, Jeong K, Koo C (2018) An optimized gene expression programming model for forecasting the national CO2 emissions in 2030 using the metaheuristic algorithms. Appl Energy 228:808–820
Pintea CM, Pop PC, Hajdu-Macelaru M (2013) Classical hybrid approaches on a transportation problem with gas emissions constraints. Adv Intell Syst Comput 188:449–458
Say NP, Yücel M (2006) Energy consumption and CO2 emissions in Turkey: empirical analysis and future projection based on an economic growth. Energy Policy 34:3870–3876
Aydin G (2015) The development and validation of regression models to predict energy-related CO2 emissions in Turkey. Energy Sources Part B 10:176–182
Şahin U (2019) Forecasting of Turkey’s electricity generation and CO2 emissions in estimating capacity factor. Environ Prog Sustain Energy 38:56–65
Köne AÇ, Büke T (2010) Forecasting of CO2 emissions from fuel combustion using trend analysis. Renew Sustain Energy Rev 14:2906–2915
Ayvaz B, Kusakci AO, Temur GT (2017) Energy-related CO2 emission forecast for Turkey and Europe and Eurasia a discrete grey model approach. Grey Syst Theory Appl 7:437–454
Hamzacebi C, Karakurt I (2015) Forecasting the energy-related CO2 emissions of Turkey using a grey prediction model. Energy Sources Part A Recovery Util Environ Eff 37:1023–1031
Pabuçcu H, Bayramoğlu T (2016) CO2 emissions forecast with neural networks with: the case of Turkey. Gazi Univ J Fac Econ Adm Sci 18:762–778
Sözen A, Gülseven Z, Arcaklioğlu E (2009) Estimation of GHG emissions in Turkey using energy and economic indicators. Energy Sources Part A 31:1141–1159
Özceylan E (2016) Forecasting CO2 emission of Turkey: swarm intelligence approaches. Int J Glob Warm 9:337–361
Uzlu E (2019) Application of Jaya algorithm-trained artificial neural networks for prediction of energy use in the nation of Turkey. Energy Sources Part B 14:183–200
Kalemci EN, İkizler SB, Dede T, Angin Z (2020) Design of reinforced concrete cantilever retaining wall using grey wolf optimization algorithm. Structures 23:245–253
Gaafary AAM, Mohamed YS, Hemeida AM, Al-Attar A, Mohamed AA (2015) Grey wolf optimization for multi input multi output system. Univers J Commun Netw 3:1–6
Hadavandi E, Mostafayi S, Soltani P (2018) A Grey Wolf Optimizer-based neural network coupled with response surface method for modeling the strength of siro-spun yarn in spinning mills. Appl Soft Comput 72:1–13
Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61
Mirjalili S (2015) How effective is the grey wolf optimizer in training multi-layer perceptrons. Appl Intell 43:150–161
Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323:533–536
Kankal M, Uzlu E, Nacar S, Yüksek Ö (2018) Predicting temporal rate coefficient of bar volume using hybrid artificial intelligence approaches. J Mar Sci Technol 23:596–604
Cinar D, Kayakutlu G, Daim T (2010) Development of future energy scenarios with intelligent algorithms: case of hydro in Turkey. Energy 35:1724–1729
Adak MF, Yumusak N (2016) Classification of e-nose aroma data of four fruit types by ABC-based neural network. Sensors 16:1–13
Sonmez M, Akgüngör AP, Bektaş S (2017) Estimating transportation energy demand in Turkey using the artificial bee colony algorithm. Energy 122:301–310
Xu Q, Chen J, Liu X, Li J, Yuan C (2017) An ABC-BP-ann algorithm for semi-active control for magnetorheological damper. KSCE J Civ Eng 21:2310–2321
Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical Report-TR06. Erciyes University Engineering Faculty Computer Engineering Department
Uzlu E, Kömürcü Mİ, Kankal M, Dede ÖHT (2014) Prediction of berm geometry using a set of laboratory tests combined with teaching–learning-based optimization and artificial bee colony algorithms. Appl Ocean Res 48:103–113
Ozkan C, Kisi O, Akay B (2011) Neural networks with artificial bee colony algorithm for modeling daily reference evapotranspiration. Irrig Sci 29:431–441
Dede T, Ayvaz Y (2015) Combined size and shape optimization of structures with a new meta-heuristic algorithm. Appl Soft Comput 28:250–258
Rao RV, More KC (2015) Optimal design of the heat pipe using TLBO (teaching–learning-based optimization algorithm). Energy 80:535–544
Rao RV, Savsani VJ, Vakharia DP (2011) Teaching-learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43:303–315
Moattari M, Moradi MH (2020) Conflict monitoring optimization heuristic inspired by brain fear and conflict systems. Int J Artif Intell 18:45–62
Precup RE, David RC, Petriu EM, Szedlak-Stinean AI, Claudia-Adina BD (2016) Grey wolf optimizer-based approach to the tuning of pi-fuzzy controllers with a reduced process parametric sensitivity. IFAC-PapersOnLine 49(5):55–60
Faris H, Aljarah I, Al-Betar MA, Mirjalili S (2018) Grey wolf optimizer: a review of recent variants and applications. Neural Comput Appl 30:413–435
Keshtegar B, Heddam S (2018) Modeling daily dissolved oxygen concentration using modified response surface method and artificial neural network: a comparative study. Neural Comput Appl 30:2995–3006
Gürlük S, Karaer F (2004) On the examination of the relation between economic growth and environmental pollution. Turk J Agric Econ 10:43–54
Halicioglu F (2009) An econometric study of CO2 emissions, energy consumption, income and foreign trade in Turkey. Energy Policy 37:1156–1164
Omay RE (2013) The relationship between environment and income: regression spline approach. Int J Energy Econ Policy 3:52–61
Acaravcı A, Öztürk İ (2010) On the relationship between energy consumption, CO2 emissions and economic growth in Europe. Energy 35:5412–5420
Akbostancı E, Aşık ST, Tunç Gİ (2009) The relationship between income and environment in Turkey: is there an environmental Kuznets curve? Energy Policy 37:861–867
Bölük G, Mert M (2015) The renewable energy, growth and environmental Kuznets curve in Turkey: An ARDL approach. Renew Sustain Energy Rev 52:587–595
Pata UK (2018) Renewable energy consumption, urbanization, financial development, income and CO2 emissions in Turkey: testing EKC hypothesis with structural breaks. J Clean Prod 187:770–779
Turkish Statistical Institute (TURKSTAT). Main statistics, Population and Demography, Population Statistics, Population by Years, Age Group and Sex, Census of Population - ABPRS. http://www.turkstat.gov.tr/UstMenu.do?metod=temelist
Republic of Turkey Presidency of Strategy and Budget (SBB). http://www.sbb.gov.tr/ekonomik-ve-sosyal-gostergeler/#1540021349004-1497d2c6-7edf
Republic of Turkey Ministry of Energy and Natural Resources (MENR): General Directorate of Electricity Affairs. Statistics, balance sheets. https://www.eigm.gov.tr/tr-TR/Denge-Tablolari/Denge-Tablolari?page=2
Republic of Turkey Ministry of Environment and Urbanization. https://cevreselgostergeler.csb.gov.tr/kentsel---kirsal-nufus-orani-i-85670
Turkish Electricity Transmission Corporation (TEIAS). Turkey’s gross electric generation by the electricity utilities and exports-imports-gross demand. https://www.teias.gov.tr/tr-TR/turkiye-elektrik-uretim-iletim-istatistikleri
Ertuğrul ÖF, Kaya Y (2017) Determining the optimal number of body-worn sensors for human activity recognition. Soft Comput 21:5053–5060
Uzlu E (2019) Estimates of energy consumption using neural networks with the grey wolf optimizer algorithm for Turkey. Gazi Üniv Fen Bilim Derg Part C Tasar ve Teknol 7:245–262 ([in Turkish])
Turkish Statistical Institute (TURKSTAT). https://data.tuik.gov.tr/Bulten/Index?p=Nufus-Projeksiyonlari-2018-2080-30567
Tefek MF, Uğuz H, Güçyetmez M (2019) A new hybrid gravitational search–teaching–learning-based optimization method for energy demand estimation of Turkey. Neural Comput Appl 31:2939–2954
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflicts of interest
The author declares that there are no conflicts of interest regarding the publication of this paper.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Uzlu, E. Estimates of greenhouse gas emission in Turkey with grey wolf optimizer algorithm-optimized artificial neural networks. Neural Comput & Applic 33, 13567–13585 (2021). https://doi.org/10.1007/s00521-021-05980-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-021-05980-1