Determination Of Variables Endangering Ecological Living By Application Of Data–Mining Models: In Case Of Turkish Geography

Abstract

The concept of ecological balance refers to the state in which various organisms coexist harmoniously within a given ecosystem. In recent times, there have been significant environmental challenges that pose a threat to the global ecosystem. It has come to attention that Turkey is experiencing adverse effects of global warming, particularly in the form of water scarcity and temperature fluctuations. Moreover, Turkey is categorized as one of the nations at risk in terms of the detrimental impacts of global warming on the environment. In light of this, the objective of this study is to employ multiple regression analysis and time series analysis to identify the variables that have a negative influence on ecological life in Turkey during the period spanning from 1960 to 2020. The findings of the regression analysis suggest that there is insufficient statistical evidence to support the notion that industrial waste and construction activities have a significant impact on carbon emissions in Turkey. Furthermore, in Turkey, various factors such as population expansion, energy consumption, electricity usage, income levels, reliance on fuel-powered cars, lack of education, energy production, and gas emissions have been identified as having a negative impact on the environment.

Keywords

• Ecological Life
• Global Warming
• Regression and Time Series Analysis
• Turkey

Ekolojik Yaşamı Tehdit Eden Değişkenlerin Veri Madenciliği Modelleri Uygulamasıyla Belirlenmesi: Türkiye Örneği

Abstract

Ekolojik denge, belirli bir ekosistemde çeşitli organizmaların uyum içinde bir arada yaşadığı durumu ifade eder. Son dönemlerde, küresel ekosistemi tehdit eden önemli çevresel sorunlar ortaya çıkmıştır. Türkiye, su kıtlığı ve sıcaklık dalgalanmaları gibi olumsuz etkilerin özellikle küresel ısınma tarafından tetiklendiğini belirtti. Küresel ısınmanın çevreye olumsuz etkileri, Türkiye'yi de tehlikeye atabilir. Bu nedenle, çalışmanın amacı, zaman serisi analizi ve çoklu regresyon analizi kullanarak 1960'tan 2020'ye kadar Türkiye'deki ekolojik yaşamı olumsuz etkileyen değişkenleri belirlemedir. Regresyon analizi, endüstriyel atık ve inşaat faaliyetlerinin Türkiye'deki karbon emisyonlarını önemli ölçüde etkilediği fikrini desteklemek için yeterli istatistiksel kanıt sağlamadığını gösterdi. Ayrıca, Türkiye'de enerji üretimi, yakıtla çalışan araçlara olan bağımlılık, nüfus artışı, elektrik tüketimi, gelir düzeyleri, yakıtla çalışan araçlara olan bağımlılık, eğitim eksikliği ve gaz emisyonları gibi çeşitli faktörlerin çevre üzerinde olumsuz etkileri olduğu tespit edilmiştir.

Keywords

• Ekolojik Yaşam
• Küresel Isınma
• Regresyon ve Zaman Serisi Analizi
• Türkiye

References

1. Akgöbek, Ö. and Çakır, F. (2009), Veri madenciliğinde bir uzman sistem tasarımı, Akademik Bilişim Konferansı Bildirileri 09-XI, 11-13 Şubat Harran Üniversitesi, Şanlıurfa, 801-806.
2. Akadiri, S.S., Alola, A. A., Olasehinde-Williams, G,, Etokakpan, M.U. (2020), The role of electricity consumption, globalization and economic growthin carbon dioxide emissions and its implications for environmentalsustainability targets. Science of The Total Environment, 708, 134653.
3. Akın, G. (2006), Küresel ısınma, nedenleri ve sonuçları, Ankara Üniversitesi Dil ve Tarih-Coğrafya Fakültesi Dergisi, 46 (2), 29-43.
4. Albayrak, M. (2008), EEG sinyallerindeki epileptiform aktivitenin veri madenciliği süreci ile tespiti, Doktora Tezi, Sakarya Üniversitesi, Fen Bilimleri Enstitüsü, Sakarya.
5. Allen, R.G.D. (1964), Statistics for economists, 133-152, Mc-Millan, UK,
6. Appenzerler, T., Dimick, R.D. (2004), Dünya alarm veriyor, National Geographic. Eylül 2004.
7. Bas, T., Kara, F. and Alola, A.A. (2021), The environmental aspects of agriculture, merchandize, share, and export value-added calibrations in Turkey. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-021-15171-z
8. Berson, A., Smith, S. and Thearling, K. (1999), Building data mining applications for CRM, Mcgraw Hill, 510, USA. Cabena, P, Hadjinian, P., Stadler, R., Verhees, J. and Zanasi, A. (1998), Discovering data mining: from concept to ımplementation. Upper Saddle River, Prentice Hall, NJ.

9. Chandio, A. A., Ozturk, I. and Akram, W. (2020), Empirical analysis of climate change factors affecting cereal yield: evidence from Turkey, Environ Sci Pollut Res., 27, 11944–11957.
10. Chatfield, C. (1996), Model uncertainty and forecast accuracy, Journal of Forecasting, 15, 7-9.
11. Chen, Z. (2001), Data mining and uncertain reasoning: An integrated approach, John Willey and Sons, Inc., 370, Canada.
12. Dogan, N. (2016), Agriculture and environmental Kuznets curves in the case of Turkey: evidence from the ARDL and bounds test, Agric. Econ. – Czech, 62, 566-574.
13. Dunham, M.H. (2003), Data mining introductory and advanced topics, New Jersey: Pearson Education, Inc.
14. Fayyad, U.M., Piatetsky-Shapiro, G. and Smyth, P. (1996), From data mining to knowledge discovery in databases, Artificial Intelligence Magazine, 17(3), 37-54.
15. Ganesh, S. (2002), Data mining: Should it be included in the ‘Statistics’ curriculum? The Sixt International Conference on Teaching Statistics, Cape Town, South Africa, 7-12.
16. Gökmenoğlu, K., Taspinar, N. (2016), The relationship between CO2 emissions, energy consumption, economic growth and FDI: The case of Turkey. J Int Trade Econ Dev 25(5), 706–723.
17. Han, J. and Kamber, M. (2001), Data mining concepts and techniques. 3rd Edition, Morgan Kaufmann Publishers.
18. IPCC (2018), Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty. In Press.
19. İnan, O. (2003), Veri madenciliği, Yüksek Lisans Tezi, Selçuk Üniversitesi, Fen Bilimleri Enstitüsü, Konya.
20. Karaca, C. (2019), Çevre ve kentleşme politikası, Ekin Yayınevi, Bursa.
21. Larose, D.T. (2005), Discovering knowledge in data, A John Willey and Sons, Inc., Publication, New Jersey.
22. Malik, M.A. (2021), Economic growth, energy consumption, and environmental quality nexus in Turkey: Evidence from simultaneous equation models. Environ Sci Pollut Res, 28, 41988–41999.
23. Miller, G.T. and Spoolman, S. (2010), Environmental science. Cengage Learning, Belmont, USA.
24. Pata, U.K. (2018), The effect of urbanization and industrialization on carbon emissions in Turkey: evidence from ARDL bounds testing procedure, Environ Sci Pollut Res, 25, 7740–7747.
25. Rokach, L. and Maimon, O. (2008), Data mining with decision trees: theory and applications, Second Edition, World Scientific, New Jersey, USA.
26. Sandy, R. (1990), Statistics for business and economics, 693-694, McGraw – Hill Education, Edition, USA.
27. Şekeroğlu, S. (2010), Hizmet sektöründe bir veri madenciliği uygulaması, Yüksek Lisans Tezi, İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, İstanbul.
28. Weiss, S.M. and Zhang, T. (2003), Performance analysis and evaluation, the handbook of data mining, Edited by Nong Ye Arizona State University, Lawrence Erlbaum Associates, Mahwah.
29. Witten, H.I., Frank, E. (2005), Data mining practical machine learning and techniques, Morgan Kaufmann Publisher, San Francisco, USA.
30. Xu, Y. (2003), Using data mining in educational research: A comparison of bayesian network with multiple regression in prediction, Department of Educational Psychology, The University of Arizona, Arizona.
31. Yıldırım, A.E. and Yıldırım, M.O. (2021), Revisiting the determinants of carbon emissions for Turkey: The role of construction sector. Environ Sci Pollut Res, 28, 42325–42338.
32. Young, G.L. (2011), Environmental encyclopedia, Gale Cengage, China.
33. Yurtkuran, S. (2021), The effect of agriculture, renewable energy production, and globalization on CO2 emissions in Turkey: A bootstrap ARDL approach, Renewable Energy, 171, 1236-1245.