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Prediction of global temperature anomaly by machine learning based techniques

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Abstract

In this work, anthropogenic and natural factors were used to evaluate and forecast climate change on a global scale by using a variety of machine-learning techniques. First, significance analysis using the Shapley method was conducted to compare the importance of each variable. Accordingly, it was determined that the equivalent CO2 concentration in the atmosphere was the most important variable, which was proposed as further evidence of climate change due to fossil fuel-based energy generation. Following that, a variety of machine learning approaches were utilized to simulate and forecast the temperature anomaly until 2100 based on six distinct scenarios. Compared to the preindustrial period, the temperature anomaly for the best-case scenario was found to increase a mean value of 1.23 °C and 1.11 °C for the mid and end of the century respectively. On the other hand, the anomaly was estimated for the worst-case scenario to reach to a mean value of 2.52 °C and 4.97 °C for the same periods. It was then concluded that machine learning approaches can assist researchers in predicting climate change and developing policies for national governments, such as committing firmly to renewable energy regulations.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  1. Mann ME, Zhang Z, Rutherford S, Bradley RS, Hughes MK, Shindell D, Ammann C, Faluvegi G, Ni F (2009) Global signatures and dynamical origins of the little ice age and medieval climate anomaly. Science 326(5957):1256–1260

    Article  Google Scholar 

  2. Botkin DB, Keller EA (2011) Environmental science: earth as a living planet, 8th edn. Wiley, United States of America

    Google Scholar 

  3. Paasche Ø, Bakke J (2010) Defining the little ice age. Clim Past 6:2159–2175

    Google Scholar 

  4. Ritchie H, Roser M (2020) Energy, <https://ourworldindata.org/energy>; [Accessed 17 August 2021] 2020

  5. Sarhadi A, Burn DH, Yang G, Ghodsi A (2016) Advances in projection of climate change impacts using supervised nonlinear dimensionality reduction techniques. Clim Dyn 48(3–4):1329–1351

    Google Scholar 

  6. Shine KP, Forster PMdF (1999) The effect of human activity on radiative forcing of climate change: a review of recent developments. Glob Planet Change 20(4):205–225

    Article  Google Scholar 

  7. NCDC (2021) Anomalies vs. Temperature, <https://www.ncdc.noaa.gov/monitoring-references/dyk/anomalies-vs-temperature >; [Accessed 23 November 2021]

  8. IPCC (2007) Climate change 2007: Synthesis report, in: R.K. Pachauri, A. Reisinger (Eds.) Geneva, Switzerland

  9. Günay ME (2016) Forecasting annual gross electricity demand by artificial neural networks using predicted values of socio-economic indicators and climatic conditions: case of Turkey. Energy Policy 90:92–101

    Article  Google Scholar 

  10. Sen D, Günay ME, Tunç KMM (2019) Forecasting annual natural gas consumption using socio-economic indicators for making future policies. Energy 173:1106–1118

    Article  Google Scholar 

  11. Sen D, Tunç KMM, Günay ME (2021) Forecasting electricity consumption of OECD countries: a global machine learning modeling approach. Utilities Policy 70:101222

    Article  Google Scholar 

  12. Feldhoff JH, Lange S, Volkholz J, Donges JF, Kurths J, Gerstengarbe F-W (2014) Complex networks for climate model evaluation with application to statistical versus dynamical modeling of South American climate. Clim Dyn 44(5–6):1567–1581

    Google Scholar 

  13. Huntingford C, Jeffers ES, Bonsall MB, Christensen HM, Lees T, Yang H (2019) Machine learning and artificial intelligence to aid climate change research and preparedness. Environ Res Lett 14(12):124007

    Article  Google Scholar 

  14. Li W, Gao X, Hao Z, Sun R (2021) Using deep learning for precipitation forecasting based on spatio-temporal information: a case study, Clim Dynam

  15. Taki M, Rohani A, Yildizhan H (2021) Application of machine learning for solar radiation modeling. Theoret Appl Climatol 143(3–4):1599–1613

    Article  Google Scholar 

  16. Zhong-Ming LU (2022) Data-driven integrated assessment of global wild-caught seafood exported to Hong Kong by 2030 in different representative concentration and shared socioeconomic pathways. Adv Clim Change Res

  17. Abbot J, Marohasy J (2017) The application of machine learning for evaluating anthropogenic versus natural climate change. GeoResJ 14:36–46

    Article  Google Scholar 

  18. Mansfield LA, Nowack PJ, Kasoar M, Everitt RG, Collins WJ, A. (2020) Voulgarakis, Predicting global patterns of long-term climate change from short-term simulations using machine learning, npj climate and Atmospheric. Science 3:44

    Google Scholar 

  19. Zheng H (2018) Analysis of global warming using machine learning, computational water, energy, and environmental. Engineering 07(03):127–141

    Google Scholar 

  20. Kalra S, Lamba R, Sharma M (2020) Machine learning based analysis for relation between global temperature and concentrations of greenhouse gases. J Inf Optim Sci 41(1):73–84

    Google Scholar 

  21. ourworldindata, Population, <https://ourworldindata.org/grapher/population>; [Accessed 14 September 2021], 2021.

  22. EEA, Atmospheric Greenhouse Gas Concentrations, <https://www.eea.europa.eu/data-and-maps/indicators/atmospheric-greenhouse-gas-concentrations-7>; [Accessed 14 September 2021], (2021).

  23. Silso, Yearly mean total sunspot number, <https://wwwbis.sidc.be/silso/infosnytot>; [Accessed 14 September 2021], (2021).

  24. LASP, Historical Total Solar Irradiance Reconstruction Time Series, <https://lasp.colorado.edu/lisird/data/historical_tsi/>; [Accessed 14 September 2021], (2021).

  25. ourworldindata, Average Temperature Anomaly Global, <https://ourworldindata.org/grapher/temperature-anomaly?country=~Global>; [accessed 14 September 2021], (2021).

  26. Hinkle DE, Wiersma W, Jurs SG (2003) Applied statistics for the behavioral sciences, 5th edn. Houghton Mifflin Harcourt, Boston

    Google Scholar 

  27. Larose DT, LCD (2014) Discovering knowledge in data: an introduction to data mining, 2 ed., John Wiley & Sons, New Jersey

  28. Suvarna M, Jahirul MI, Aaron-Yeap WH, Augustine CV, Umesh A, Rasul MG, Günay ME, Yildirim R, Janaun J (2022) Predicting biodiesel properties and its optimal fatty acid profile via explainable machine learning. Renew Energy 189:245–258

    Article  Google Scholar 

  29. Cohen S, Ruppin E, Dror G (2005) Feature selection based on the shapley value, proceedings of the nineteenth international joint conference on artificial intelligence, Edinburgh, Scotland, UK

  30. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Article  Google Scholar 

  31. Yu Y, Si X, Hu C, Zhang J (2019) A review of recurrent neural networks: LSTM cells and network architectures. Neural Comput 31(7):1235–1270

    Article  MathSciNet  MATH  Google Scholar 

  32. Bilgili M, Yildirim A, Ozbek A, Celebi K, Ekinci F (2020) Long short-term memory (LSTM) neural network and adaptive neuro-fuzzy inference system (ANFIS) approach in modeling renewable electricity generation forecasting. Int J Green Energy 18(6):578–594

    Article  Google Scholar 

  33. Memarzadeh G, Keynia F (2020) A new short-term wind speed forecasting method based on fine-tuned LSTM neural network and optimal input sets. Energy Convers Manag 213:112824

    Article  Google Scholar 

  34. Chen Z-T, Liu H-Y, Xu C-Y, Wu X-C, Liang B-Y, Cao J, Chen D (2022) Deep learning projects future warming-induced vegetation growth changes under SSP scenarios. Adv Clim Chang Res 13(2):251–257

    Article  Google Scholar 

  35. Gao B, Huang X, Shi J, Tai Y, Zhang J (2020) Hourly forecasting of solar irradiance based on CEEMDAN and multi-strategy CNN-LSTM neural networks. Renew Energy 162:1665–1683

    Article  Google Scholar 

  36. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay É (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830

    MathSciNet  MATH  Google Scholar 

  37. Chen T, Guestrin (2016) XGBoost, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794.

  38. Chen T, Guestrin C (2016) XGBoost: A scalable tree boosting system, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery, San Francisco, pp. 785–794.

  39. xgboost, XGBoost Parameters, <https://xgboost.readthedocs.io/en/latest/parameter.html>; [accessed 11 November 2021], 2021.

  40. Dangeti P (2017) Statistics for machine learning. Packt Publishing, Birmingham

    Google Scholar 

  41. Liaw A, W. M. (2002) Classification and regression by random. Forest, R News 2(3) 18–22

  42. Segal MR (2004) Machine learning benchmarks and random forest regression, UCSF: Center for bioinformatics and molecular biostatistics https://escholarship.org/uc/item/35x3v9t4

  43. Fathi E, Shoja BM (2018) Deep neural networks for natural language processing, in: V.N. Gudivada, C.R. Rao (Eds.), Handbook of Statistics, Elsevier2018, pp. 229–316.

  44. Larose DT, Larose CD (2014) Discovering knowledge in data: an introduction to data mining, 2nd edn. John Wiley & Sons, New Jersey

    MATH  Google Scholar 

  45. Abdel-Rahman HI, Marzouk BA (2019) Statistical method to predict the sunspots number. NRIAG J Astron Geophys 7(2):175–179

    Article  Google Scholar 

  46. van Vuuren DP, den Elzen MGJ, Lucas PL, Eickhout B, Strengers BJ, van Ruijven B, Wonink S, van Houdt R (2007) Stabilizing greenhouse gas concentrations at low levels: an assessment of reduction strategies and costs. Clim Change 81(2):119–159

    Article  Google Scholar 

  47. Wise M, Calvin K, Thomson A, Clarke L, Bond-Lamberty B, Sands R, Smith SJ, Janetos A, Edmonds J (2009) Implications of limiting CO2 concentrations for land use and energy. Science 324(5931):1183–1186

    Article  Google Scholar 

  48. Hijioka Y, Matsuoka Y, Nishimoto H, Masui T, Kainuma M (2008) Global GHG emission scenarios under GHG concentration stabilization targets. J Glob Environ Eng 13:97–108

    Google Scholar 

  49. Riahi K, Grübler A, Nakicenovic N (2007) Scenarios of long-term socio-economic and environmental development under climate stabilization. Technol Forecast Soc Chang 74(7):887–935

    Article  Google Scholar 

  50. Schurer AP, Mann ME, Hawkins E, Tett SFB, Hegerl GC (2017) Importance of the pre-industrial baseline in determining the likelihood of exceeding the paris limits. Nat Clim Chang 7(8):563–567

    Article  Google Scholar 

  51. UNFCCC, What is the Kyoto Protocol?, <https://unfccc.int/kyoto_protocol>; [Accessed 11 November 2021], 2021.

  52. UNFCCC, The Paris Agreement, <https://unfccc.int/process-and-meetings/the-paris-agreement/the-paris-agreement>; [Accessed 11 November 2021], 2021.

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Authors and Affiliations

  1. Department of Industrial Engineering, Istanbul Bilgi University, 34060, Eyupsultan-Istanbul, Turkey

    Doruk Sen

  2. Faculty of Maritime Studies, University of Kyrenia, Girne, Cyprus

    Mehmet Fatih Huseyinoglu

  3. Biosphere Research Center, Istanbul, Turkey

    Mehmet Fatih Huseyinoglu

  4. Department of Energy Systems Engineering, Istanbul Bilgi University, 34060, Eyupsultan-Istanbul, Turkey

    M. Erdem Günay

Authors
  1. Doruk Sen
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  2. Mehmet Fatih Huseyinoglu
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  3. M. Erdem Günay
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Correspondence to Doruk Sen.

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Sen, D., Huseyinoglu, M.F. & Günay, M.E. Prediction of global temperature anomaly by machine learning based techniques. Neural Comput & Applic 35, 15601–15614 (2023). https://doi.org/10.1007/s00521-023-08580-3

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