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Forecasting of Greenhouse Gas Emissions in Turkey using Machine Learning Methods

Year 2020, Volume: 8 Issue: 2, 332 - 348, 26.05.2020
https://doi.org/10.21541/apjes.658922

Abstract

Greenhouse gas emissions prevent our world's self-renewal capacity and cause ozone depletion, global warming and reduced food resources. In addition, greenhouse gases are the biggest factor that creates the ecological footprint. To make the world more livable and self-sufficient, the biocapacity fields and the ecological footprint must be in balance. In order to achieve this balance, the situation for the future of greenhouse gas emissions should be determined. In this study, the forecasting of greenhouse gas emissions for Turkey is carried out using machine learning algorithms, and the data set denominated greenhouse gas emissions of Turkey between the years 1967-2017. In order to test the success of the methods, the data set is first handled as a time series and then 10-fold cross-validation is applied to evaluate the results statistically. Long Short-Term Memory is determined as the best algorithm and in the test set evaluated as time series, root mean square error, mean absolute percentage error and the coefficient of determination of this algorithm are found as 0.25, 1.11, and 1.0 respectively. The model created with these successful results is used to estimate greenhouse gas emissions between 2018 and 2031. Forecasted emission values are at a high level compared to today, and necessary measures and activities to increase biomass should be carried out considering these values.

References

  • [1]. Ritchie H., Roser M, CO2 and Greenhouse Gas Emissions. Published online at OurWorldInData.org, URL: https://ourworldindata.org/co2-and-other-greenhouse-gas-emissions (Erişim zamanı; Eylül, 1, 2019).
  • [2]. Chicherin, S, “Low-Temperature District Heating With Decentralized Generation By Heat Pumps At A Railway Station: Optimizing The System And Calculating Greenhouse Gas Emissions,” Innovations, vol.6, no 2, pp. 82-84, 2018.
  • [3]. Chen, H., Awasthi, M. K., Liu, T., Zhao, J., Ren, X., Wang, M., Duan, Y., Awasthi, S.K., Zhang, Z, “Influence Of Clay As Additive On Greenhouse Gases Emission And Maturity Evaluation During Chicken Manure Composting,”, Bioresource technology, vol. 266, pp. 82-88, 2018.
  • [4]. Gülhan, H., Özgün, H., Erşahin, M. E., Dereli, R. K., Öztürk, İ, “İstanbul’daki Biyolojik Atıksu Arıtma Tesislerinin Sera Gazı Emisyonunun Modelleme Metodu ile Tahmini,”, Fırat Üniversitesi Mühendislik Bilimleri Dergisi, vol. 30, no 1, pp. 59-67, 2018.
  • [5]. Räsänen, T. A., Varis, O., Scherer, L., Kummu, M, “Greenhouse Gas Emissions Of Hydropower In The Mekong River Basin,”, Environmental Research Letters, vol. 13, no 3, 034030, 2018.
  • [6]. Baran, M. F., Karaağaç, H. A., Mart, D., Bolat, A., Eren, Ö, “Nohut Üretiminde Enerji Kullanım Etkinliği Ve Sera Gazı (Ghg) Emisyonunun Belirlenmesi (Adana Ili Örneği),”, Avrupa Bilim Ve Teknoloji Dergisi, vol. 16, pp. 41-50, 2019.
  • [7]. Dulkadiroğlu, H. “Türkiye’de Elektrik Üretiminin Sera Gazi Emisyonlari Açisindan İncelenmesi,”, Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, vol. 7, no 1, pp. 67-74, 2018.
  • [8]. Henderson, B., Golub, A., Pambudi, D., Hertel, T., Godde, C., Herrero, M., Cacho, O., Gerber, P, “The Power And Pain Of Market-Based Carbon Policies: A Global Application To Greenhouse Gases From Ruminant Livestock Production,”, Mitigation And Adaptation Strategies For Global Change, vol. 23, no 3, pp. 349-369, 2018.
  • [9]. Orhan, A. E, “Çimento Üretiminden Kaynaklanan Sera Gazı Emisyonlarının Hesaplanması”, MSc thesis, Hacettepe Üniversitesi Fen Bilimleri Enstitüsü, Ankara/Türkiye, 2018.
  • [10]. Tokay, Z, “Türkiye'nin Çeltik Yetiştiriciliği Kaynaklı Sera Gazı Emisyonlarının Değerlendirilmesi”, MSc thesis, Hacettepe Üniversitesi Fen Bilimleri Enstitüsü, Ankara/Türkiye, 2018.
  • [11]. Zhang, Z., Li, H., Chang, H., Pan, Z., Luo, X, “Machine Learning Predictive Framework For Co2 Thermodynamic Properties In Solution,”, Journal Of Co2 Utilization, vol. 26, pp. 152-159, 2018.
  • [12]. Sefiner, I, “Some Artificial Neural Network Applications To Greenhouse Environmental Control,”, Computers And Electronics In Agriculture, vol. 18 no 2–3, pp. 167-186, 1997.
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  • [14]. Nabavi-Pelesaraei, A., Abdi, R., Rafiee, S., Shamshirband, S., Yousefinejad-Ostadkelayeh, M, “Resource Management In Cropping Systems Using Artificial Intelligence Techniques: A Case Study Of Orange Orchards In North Of Iran,”, Stochastic Environmental Research And Risk Assessment, vol. 30, no 1, pp. 413-427, 2016.
  • [15]. Nguyen, T. B., Schoepp, W., Wagner, F, “Gains-Bi: Business Intelligent Approach For Greenhouse Gas And Air Pollution Interactions And Synergies Information System,”, In Proceedings Of The 10th International Conference On Information Integration And Web-Based Applications & Services, November 2008, Acm, 332-338, (2008).
  • [16]. Nguyen, K. A., Sahin, O., Stewart, R. A., Zhang, H, “Smart Technologies In Reducing Carbon Emission: Artificial Intelligence And Smart Water Meter,”, In Proceedings Of The 9th International Conference On Machine Learning And Computing, February 2017, Acm, 517-522, (2017).
  • [17]. Nguyen, T. B., Wagner, F., Schoepp, W, “Cloud Intelligent Services For Calculating Emissions And Costs Of Air Pollutants And Greenhouse Gases,”, In Asian Conference On Intelligent Information And Database Systems Springer, April 2011, Berlin, Heidelberg, 159-168, (2011).
  • [18]. Behrang, M. A., Assareh, E., Assari, M. R., Ghanbarzadeh, A, “Using Bees Algorithm And Artificial Neural Network To Forecast World Carbon Dioxide Emission,”, Energy Sources, Part A: Recovery, Utilization, And Environmental Effects, vol. 33, no 19, pp. 1747-1759, 2011.
  • [19]. Nabavi-Pelesaraei, A., Rafiee, S., Mohtasebi, S. S., Hosseinzadeh-Bandbafha, H., Chau, K. W, “Integration Of Artificial Intelligence Methods And Life Cycle Assessment To Predict Energy Output And Environmental Impacts Of Paddy Production,”, Science Of The Total Environment, vol. 631, pp. 1279-1294, 2018.
  • [20]. Hosseinzadeh-Bandbafha, H., Nabavi-Pelesaraei, A., & Shamshirband, S, “Investigations Of Energy Consumption And Greenhouse Gas Emissions Of Fattening Farms Using Artificial Intelligence Methods,”, Environmental Progress & Sustainable Energy, vol. 36, no 5, pp. 1546-1559, 2017.
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  • [22]. Cameron, A.C., Trivedi, P.K. Regression Analysis Of Count Data. New York: Cambridge University Press, Ny, 1998.
  • [23]. Goodfellow, I., Bengio, Y. Courville, A. Deep Learning. Cambridge, Ma: Mit Press, 2016.
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Makine Öğrenimi Yöntemleri ile Türkiye için Sera Gazı Emisyonu Tahmini

Year 2020, Volume: 8 Issue: 2, 332 - 348, 26.05.2020
https://doi.org/10.21541/apjes.658922

Abstract

Sera gazı emisyonu dünyamızın kendini yenileme kapasitesinin önüne geçerek, ozon tabakasının delinmesi, küresel ısınma ve besin kaynaklarının azalması gibi sonuçlara sebep olmaktadır. Ayrıca sera gazları, ekolojik ayak izini oluşturan en büyük etmendir. Dünyanın daha yaşanılabilir ve kendi kendine yetebilir olması için biyokütle alanları ile ekolojik ayak izi dengede olmalıdır. Bu dengeyi sağlamak için ise sera gazı emisyonunun ileriye yönelik durumu belirlenmelidir. Bu çalışmada, makine öğrenimi algoritmaları kullanılarak Türkiye için ileriye yönelik sera gazı emisyonu tahminlemesi gerçekleştirilmiş olup, veri setini Türkiye’ye ait 1967-2017 yılları arasındaki sera gazı emisyonu oluşturmaktadır. Yöntemlerin başarısını sınamak için öncelikle veri seti zaman serisi olarak ele alınmış daha sonra ise istatistiksel olarak da sonuçları değerlendirmek için 10-kat çapraz doğrulama uygulanmıştır. En iyi algoritma olarak Uzun Kısa-Vadeli Hafıza tespit edilmiş olup zaman serisi olarak değerlendirilen test setinde bu algoritmanın ortalama karesel hataların karekökü, ortalama mutlak yüzde hata ve belirleme katsayısı değerleri sırası ile 0.25, 1.11, 1.0 bulunmuştur. Bu başarılı sonuçlar ile oluşturulan model 2018-2031 yılına kadar olan sera gazı emisyonunu tahmin etmek için kullanılmıştır. Tahmin edilen emisyon değerleri günümüze göre yüksek seviyede olup bu değerler göz önüne alınarak gerekli tedbir ve biyokütleyi artırıcı faaliyetlerin gerçekleştirilmesi gerekmektedir.

References

  • [1]. Ritchie H., Roser M, CO2 and Greenhouse Gas Emissions. Published online at OurWorldInData.org, URL: https://ourworldindata.org/co2-and-other-greenhouse-gas-emissions (Erişim zamanı; Eylül, 1, 2019).
  • [2]. Chicherin, S, “Low-Temperature District Heating With Decentralized Generation By Heat Pumps At A Railway Station: Optimizing The System And Calculating Greenhouse Gas Emissions,” Innovations, vol.6, no 2, pp. 82-84, 2018.
  • [3]. Chen, H., Awasthi, M. K., Liu, T., Zhao, J., Ren, X., Wang, M., Duan, Y., Awasthi, S.K., Zhang, Z, “Influence Of Clay As Additive On Greenhouse Gases Emission And Maturity Evaluation During Chicken Manure Composting,”, Bioresource technology, vol. 266, pp. 82-88, 2018.
  • [4]. Gülhan, H., Özgün, H., Erşahin, M. E., Dereli, R. K., Öztürk, İ, “İstanbul’daki Biyolojik Atıksu Arıtma Tesislerinin Sera Gazı Emisyonunun Modelleme Metodu ile Tahmini,”, Fırat Üniversitesi Mühendislik Bilimleri Dergisi, vol. 30, no 1, pp. 59-67, 2018.
  • [5]. Räsänen, T. A., Varis, O., Scherer, L., Kummu, M, “Greenhouse Gas Emissions Of Hydropower In The Mekong River Basin,”, Environmental Research Letters, vol. 13, no 3, 034030, 2018.
  • [6]. Baran, M. F., Karaağaç, H. A., Mart, D., Bolat, A., Eren, Ö, “Nohut Üretiminde Enerji Kullanım Etkinliği Ve Sera Gazı (Ghg) Emisyonunun Belirlenmesi (Adana Ili Örneği),”, Avrupa Bilim Ve Teknoloji Dergisi, vol. 16, pp. 41-50, 2019.
  • [7]. Dulkadiroğlu, H. “Türkiye’de Elektrik Üretiminin Sera Gazi Emisyonlari Açisindan İncelenmesi,”, Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, vol. 7, no 1, pp. 67-74, 2018.
  • [8]. Henderson, B., Golub, A., Pambudi, D., Hertel, T., Godde, C., Herrero, M., Cacho, O., Gerber, P, “The Power And Pain Of Market-Based Carbon Policies: A Global Application To Greenhouse Gases From Ruminant Livestock Production,”, Mitigation And Adaptation Strategies For Global Change, vol. 23, no 3, pp. 349-369, 2018.
  • [9]. Orhan, A. E, “Çimento Üretiminden Kaynaklanan Sera Gazı Emisyonlarının Hesaplanması”, MSc thesis, Hacettepe Üniversitesi Fen Bilimleri Enstitüsü, Ankara/Türkiye, 2018.
  • [10]. Tokay, Z, “Türkiye'nin Çeltik Yetiştiriciliği Kaynaklı Sera Gazı Emisyonlarının Değerlendirilmesi”, MSc thesis, Hacettepe Üniversitesi Fen Bilimleri Enstitüsü, Ankara/Türkiye, 2018.
  • [11]. Zhang, Z., Li, H., Chang, H., Pan, Z., Luo, X, “Machine Learning Predictive Framework For Co2 Thermodynamic Properties In Solution,”, Journal Of Co2 Utilization, vol. 26, pp. 152-159, 2018.
  • [12]. Sefiner, I, “Some Artificial Neural Network Applications To Greenhouse Environmental Control,”, Computers And Electronics In Agriculture, vol. 18 no 2–3, pp. 167-186, 1997.
  • [13]. Kargupta, H., Gama, J., Fan, W, “The Next Generation Of Transportation Systems, Greenhouse Emissions, And Data Mining,”, In Proceedings Of The 16th Acm Sigkdd International Conference On Knowledge Discovery And Data Mining, July 2010, Acm, 1209-1212, (2010).
  • [14]. Nabavi-Pelesaraei, A., Abdi, R., Rafiee, S., Shamshirband, S., Yousefinejad-Ostadkelayeh, M, “Resource Management In Cropping Systems Using Artificial Intelligence Techniques: A Case Study Of Orange Orchards In North Of Iran,”, Stochastic Environmental Research And Risk Assessment, vol. 30, no 1, pp. 413-427, 2016.
  • [15]. Nguyen, T. B., Schoepp, W., Wagner, F, “Gains-Bi: Business Intelligent Approach For Greenhouse Gas And Air Pollution Interactions And Synergies Information System,”, In Proceedings Of The 10th International Conference On Information Integration And Web-Based Applications & Services, November 2008, Acm, 332-338, (2008).
  • [16]. Nguyen, K. A., Sahin, O., Stewart, R. A., Zhang, H, “Smart Technologies In Reducing Carbon Emission: Artificial Intelligence And Smart Water Meter,”, In Proceedings Of The 9th International Conference On Machine Learning And Computing, February 2017, Acm, 517-522, (2017).
  • [17]. Nguyen, T. B., Wagner, F., Schoepp, W, “Cloud Intelligent Services For Calculating Emissions And Costs Of Air Pollutants And Greenhouse Gases,”, In Asian Conference On Intelligent Information And Database Systems Springer, April 2011, Berlin, Heidelberg, 159-168, (2011).
  • [18]. Behrang, M. A., Assareh, E., Assari, M. R., Ghanbarzadeh, A, “Using Bees Algorithm And Artificial Neural Network To Forecast World Carbon Dioxide Emission,”, Energy Sources, Part A: Recovery, Utilization, And Environmental Effects, vol. 33, no 19, pp. 1747-1759, 2011.
  • [19]. Nabavi-Pelesaraei, A., Rafiee, S., Mohtasebi, S. S., Hosseinzadeh-Bandbafha, H., Chau, K. W, “Integration Of Artificial Intelligence Methods And Life Cycle Assessment To Predict Energy Output And Environmental Impacts Of Paddy Production,”, Science Of The Total Environment, vol. 631, pp. 1279-1294, 2018.
  • [20]. Hosseinzadeh-Bandbafha, H., Nabavi-Pelesaraei, A., & Shamshirband, S, “Investigations Of Energy Consumption And Greenhouse Gas Emissions Of Fattening Farms Using Artificial Intelligence Methods,”, Environmental Progress & Sustainable Energy, vol. 36, no 5, pp. 1546-1559, 2017.
  • [21]. Ncss Statistical System Software User’s Guide. Ncss, Utah, Usa. Published Online At Www.Ncss.Com. URL: Https://Www.Ncss.Com/Software/Ncss/Ncss-Documentation/ (Erişim zamanı; Eylül, 8, 2019).
  • [22]. Cameron, A.C., Trivedi, P.K. Regression Analysis Of Count Data. New York: Cambridge University Press, Ny, 1998.
  • [23]. Goodfellow, I., Bengio, Y. Courville, A. Deep Learning. Cambridge, Ma: Mit Press, 2016.
  • [24]. Microsoft Azure Machine Learning Studio. URL: Https://Studio.Azureml.Net/ (Erişim zamanı; Eylül, 1, 2019).
There are 24 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Melike Şişeci Çeşmeli 0000-0001-9541-2590

İhsan Pençe 0000-0003-0734-3869

Publication Date May 26, 2020
Submission Date December 13, 2019
Published in Issue Year 2020 Volume: 8 Issue: 2

Cite

IEEE M. Şişeci Çeşmeli and İ. Pençe, “Makine Öğrenimi Yöntemleri ile Türkiye için Sera Gazı Emisyonu Tahmini”, APJES, vol. 8, no. 2, pp. 332–348, 2020, doi: 10.21541/apjes.658922.