Evidence and Prediction Regarding the Continuous Global Warming: A Severe Domino Effect

Junhyeop Cho; Gyeongseung Han; Christopher Jeongchan Lee; Suh Hyun Lee; Aaron Youngwoo Yoo

doi:doi:10.11648/j.ajese.20210504.11

| Peer-Reviewed

Evidence and Prediction Regarding the Continuous Global Warming: A Severe Domino Effect

Junhyeop Cho, Gyeongseung Han, Christopher Jeongchan Lee, Suh Hyun Lee, Aaron Youngwoo Yoo

Published in American Journal of Environmental Science and Engineering (Volume 5, Issue 4)

Received: 23 September 2021 Accepted: 5 October 2021 Published: 12 October 2021

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

Global warming has been a major threat to Earth for decades; still, this issue has not been taken seriously by many. Although it is proven that one of its main causes is human activity, humanity’s effort towards a safer, healthier planet has been minimal. After years of neglect, global warming has worsened, and its adverse effects have become more severe. This paper aims to underscore the necessity of human efforts and universal contribution to subside the devastating ramifications of global warming. To investigate the past, present, and possible future consequences of global warming, this paper analyzes data mostly obtained from the United States Environmental Protection Agency (EPA). The paper also presents graphs that clearly illustrate the increases in global sea levels, permafrost temperatures, sea surface temperatures, and concentrations of greenhouse gases. Furthermore, the paper utilizes a linear regression machine learning algorithm, a method widely used by researchers to create predictive models, to depict future trends of the data of the aforementioned subjects. This analysis and visualization of data conclude that a so-called “domino effect” was certainly present as some environmental changes of global warming. To solve the problem of global warming, the paper finally uses the K-neighbor regression method in Python to predict the amount of power generated in the solar power systems of Berkeley, California in an accurate, flexible way.

Published in	American Journal of Environmental Science and Engineering (Volume 5, Issue 4)
DOI	10.11648/j.ajese.20210504.11
Page(s)	76-86
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Global Warming, Linear Regression, K-neighbor Regression, Python, Machine Learning, Data Analysis

References

[1]	Brown, K. (2020, January 9). 5 Global Issues to Watch in 2020. unfoundation.org. https://unfoundation.org/blog/post/5-global-issues-to-watch-in-2020/.
[2]	Impacts of global warming. WWF. (n.d.). https://www.wwf.or g.au/what-we-do/climate/impacts-of-global-warming#gs.44luzt.
[3]	NASA. (2021, June 16). The Causes of Climate Change. NASA. https://climate.nasa.gov/causes/.
[4]	Causes of Global Warming: WWF-Australia. WWF. (n.d.). https://www.wwf.org.au/what-we-do/climate/causes-of-global-warming#gs.44m9ib.
[5]	Ritchie, H., & amp; Roser, M. (2017, October 2). Fossil Fuels. Our World in Data. https://ourworldindata.org/fossil-fuels.
[6]	Lin C. (2021). Fossil fuel. In World Book Advanced. https://www.worldbookonline.com/advanced/article?id=ar207535.
[7]	Ramirez, R. (2021, June 18). The amount of heat the Earth traps has doubled in just 15 years, study shows. CNN. https://edition.cnn.com/2021/06/17/us/earth-trapped-heat-doubled/index.html.
[8]	Mastrandrea, M. D. (2021). Global warming. In World Book Advanced. https://www.worldbookonline.com/advanced/article?id=ar226310.
[9]	Staub, R. (2019, January 9). Fight Climate Change. Turtle Island Restoration Network. https://seaturtles.org/our-work/ou r-programs/fighting-climate-change/?gclid=CjwKCAjwieuGBhAsEiwA1Ly_nXgdlBR_LaBmKrRVkwpSYSLPc0vmPcL91sGkBhsvn27rBC_uLyi75RoCR6oQAvD_BwE.
[10]	Glick, D. (2021, May 3). Global Climate Change, Melting Glaciers. Environment. http://www.nationalgeographic.com/environment/global-warming/big-thaw/.
[11]	Lindsey, R. (2021, January 25). Climate Change: Global Sea Level: NOAA Climate.gov. Climate Change: Global Sea Level \| NOAA Climate.gov. https://www.climate.gov/news-features/understanding-climate/climate-change-global-sea-level.
[12]	Roy, E. A. (2019, May 16). 'One day we'll disappear': Tuvalu's sinking islands \| Eleanor Ainge Roy. The Guardian. https://www.theguardian.com/global-development/2019/may/16/one-day-disappear-tuvalu-sinking-islands-rising-seas-climate-change.
[13]	Rice, D. (2020, February 14). One-third of all plant and animal species could be extinct in 50 years, study warns. USA Today. https://www.usatoday.com/story/news/nation/2020/02/14/climate-change-study-plant-animal-extinction/4760646002/.
[14]	Bliss, L. C. (2021). Permafrost. In World Book Advanced. https://www.worldbookonline.com/advanced/article?id=ar423700.
[15]	Strehl, A., & Littman, M. (2007). Online linear regression and its application to model-based reinforcement learning. Advances in Neural Information Processing Systems, 20, 1417-1424.
[16]	Nguyen, Van Lam, Nguyen, Hoang Dat, Cho, Yong-Soon, Kim, Ho-Sook, Han, Il-Yong, Kim, Dae-Kyeong,. Shin, Jae-Gook. (2021). Comparison of multivariate linear regression and a machine learning algorithm developed for prediction of precision warfarin dosing in a Korean population. Journal of Thrombosis and Haemostasis, Journal of thrombosis and haemostasis, 2021-03-28.
[17]	Solar Energy Industries Association. (n.d.). About Solar Energy. SEIA. https://www.seia.org/initiatives/about-solar-ene rgy.
[18]	Howell, B. (2021, June 22). Solar Panel Electricity Output: February 2021. The Eco Experts. https://www.theecoexperts.co.uk/solar-panels/electricity-power-output.
[19]	Mohd. S, Sia. S, San. Wag, and Sub. Lab (2017), A Review paper on Electricity generation from solar energy. International Journal for Research in Applied Science & Engineering Technology (IJRASET).
[20]	Steorts, R. C. (n.d.). Comparison of Linear Regression with K-Nearest Neighbors. Chapter 3.5 ISL. Duke University; Duke University.

Cite This Article

Plain Text BibTeX RIS

APA Style

Junhyeop Cho, Gyeongseung Han, Christopher Jeongchan Lee, Suh Hyun Lee, Aaron Youngwoo Yoo. (2021). Evidence and Prediction Regarding the Continuous Global Warming: A Severe Domino Effect. American Journal of Environmental Science and Engineering, 5(4), 76-86. https://doi.org/10.11648/j.ajese.20210504.11

Copy | Download

ACS Style

Junhyeop Cho; Gyeongseung Han; Christopher Jeongchan Lee; Suh Hyun Lee; Aaron Youngwoo Yoo. Evidence and Prediction Regarding the Continuous Global Warming: A Severe Domino Effect. Am. J. Environ. Sci. Eng. 2021, 5(4), 76-86. doi: 10.11648/j.ajese.20210504.11

Copy | Download

AMA Style

Junhyeop Cho, Gyeongseung Han, Christopher Jeongchan Lee, Suh Hyun Lee, Aaron Youngwoo Yoo. Evidence and Prediction Regarding the Continuous Global Warming: A Severe Domino Effect. Am J Environ Sci Eng. 2021;5(4):76-86. doi: 10.11648/j.ajese.20210504.11

Copy | Download

@article{10.11648/j.ajese.20210504.11,
  author = {Junhyeop Cho and Gyeongseung Han and Christopher Jeongchan Lee and Suh Hyun Lee and Aaron Youngwoo Yoo},
  title = {Evidence and Prediction Regarding the Continuous Global Warming: A Severe Domino Effect},
  journal = {American Journal of Environmental Science and Engineering},
  volume = {5},
  number = {4},
  pages = {76-86},
  doi = {10.11648/j.ajese.20210504.11},
  url = {https://doi.org/10.11648/j.ajese.20210504.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajese.20210504.11},
  abstract = {Global warming has been a major threat to Earth for decades; still, this issue has not been taken seriously by many. Although it is proven that one of its main causes is human activity, humanity’s effort towards a safer, healthier planet has been minimal. After years of neglect, global warming has worsened, and its adverse effects have become more severe. This paper aims to underscore the necessity of human efforts and universal contribution to subside the devastating ramifications of global warming. To investigate the past, present, and possible future consequences of global warming, this paper analyzes data mostly obtained from the United States Environmental Protection Agency (EPA). The paper also presents graphs that clearly illustrate the increases in global sea levels, permafrost temperatures, sea surface temperatures, and concentrations of greenhouse gases. Furthermore, the paper utilizes a linear regression machine learning algorithm, a method widely used by researchers to create predictive models, to depict future trends of the data of the aforementioned subjects. This analysis and visualization of data conclude that a so-called “domino effect” was certainly present as some environmental changes of global warming. To solve the problem of global warming, the paper finally uses the K-neighbor regression method in Python to predict the amount of power generated in the solar power systems of Berkeley, California in an accurate, flexible way.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Evidence and Prediction Regarding the Continuous Global Warming: A Severe Domino Effect
AU  - Junhyeop Cho
AU  - Gyeongseung Han
AU  - Christopher Jeongchan Lee
AU  - Suh Hyun Lee
AU  - Aaron Youngwoo Yoo
Y1  - 2021/10/12
PY  - 2021
N1  - https://doi.org/10.11648/j.ajese.20210504.11
DO  - 10.11648/j.ajese.20210504.11
T2  - American Journal of Environmental Science and Engineering
JF  - American Journal of Environmental Science and Engineering
JO  - American Journal of Environmental Science and Engineering
SP  - 76
EP  - 86
PB  - Science Publishing Group
SN  - 2578-7993
UR  - https://doi.org/10.11648/j.ajese.20210504.11
AB  - Global warming has been a major threat to Earth for decades; still, this issue has not been taken seriously by many. Although it is proven that one of its main causes is human activity, humanity’s effort towards a safer, healthier planet has been minimal. After years of neglect, global warming has worsened, and its adverse effects have become more severe. This paper aims to underscore the necessity of human efforts and universal contribution to subside the devastating ramifications of global warming. To investigate the past, present, and possible future consequences of global warming, this paper analyzes data mostly obtained from the United States Environmental Protection Agency (EPA). The paper also presents graphs that clearly illustrate the increases in global sea levels, permafrost temperatures, sea surface temperatures, and concentrations of greenhouse gases. Furthermore, the paper utilizes a linear regression machine learning algorithm, a method widely used by researchers to create predictive models, to depict future trends of the data of the aforementioned subjects. This analysis and visualization of data conclude that a so-called “domino effect” was certainly present as some environmental changes of global warming. To solve the problem of global warming, the paper finally uses the K-neighbor regression method in Python to predict the amount of power generated in the solar power systems of Berkeley, California in an accurate, flexible way.
VL  - 5
IS  - 4
ER  -

Copy | Download

Author Information

Junhyeop Cho

Mercersburg Academy, Mercersburg, Pennsylvania, United States of America
Gyeongseung Han

Chadwick International, Incheon, Republic of Korea
Christopher Jeongchan Lee

Seoul International School, Seoul, Republic of Korea
Suh Hyun Lee

Yongsan International School of Seoul, Seoul, Republic of Korea
Aaron Youngwoo Yoo

Dulwich College Seoul, Seoul, Republic of Korea

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Junhyeop Cho, Gyeongseung Han, Christopher Jeongchan Lee, Suh Hyun Lee, Aaron Youngwoo Yoo. (2021). Evidence and Prediction Regarding the Continuous Global Warming: A Severe Domino Effect. American Journal of Environmental Science and Engineering, 5(4), 76-86. https://doi.org/10.11648/j.ajese.20210504.11

Copy | Download

ACS Style

Junhyeop Cho; Gyeongseung Han; Christopher Jeongchan Lee; Suh Hyun Lee; Aaron Youngwoo Yoo. Evidence and Prediction Regarding the Continuous Global Warming: A Severe Domino Effect. Am. J. Environ. Sci. Eng. 2021, 5(4), 76-86. doi: 10.11648/j.ajese.20210504.11

Copy | Download

AMA Style

Junhyeop Cho, Gyeongseung Han, Christopher Jeongchan Lee, Suh Hyun Lee, Aaron Youngwoo Yoo. Evidence and Prediction Regarding the Continuous Global Warming: A Severe Domino Effect. Am J Environ Sci Eng. 2021;5(4):76-86. doi: 10.11648/j.ajese.20210504.11

Copy | Download

@article{10.11648/j.ajese.20210504.11,
  author = {Junhyeop Cho and Gyeongseung Han and Christopher Jeongchan Lee and Suh Hyun Lee and Aaron Youngwoo Yoo},
  title = {Evidence and Prediction Regarding the Continuous Global Warming: A Severe Domino Effect},
  journal = {American Journal of Environmental Science and Engineering},
  volume = {5},
  number = {4},
  pages = {76-86},
  doi = {10.11648/j.ajese.20210504.11},
  url = {https://doi.org/10.11648/j.ajese.20210504.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajese.20210504.11},
  abstract = {Global warming has been a major threat to Earth for decades; still, this issue has not been taken seriously by many. Although it is proven that one of its main causes is human activity, humanity’s effort towards a safer, healthier planet has been minimal. After years of neglect, global warming has worsened, and its adverse effects have become more severe. This paper aims to underscore the necessity of human efforts and universal contribution to subside the devastating ramifications of global warming. To investigate the past, present, and possible future consequences of global warming, this paper analyzes data mostly obtained from the United States Environmental Protection Agency (EPA). The paper also presents graphs that clearly illustrate the increases in global sea levels, permafrost temperatures, sea surface temperatures, and concentrations of greenhouse gases. Furthermore, the paper utilizes a linear regression machine learning algorithm, a method widely used by researchers to create predictive models, to depict future trends of the data of the aforementioned subjects. This analysis and visualization of data conclude that a so-called “domino effect” was certainly present as some environmental changes of global warming. To solve the problem of global warming, the paper finally uses the K-neighbor regression method in Python to predict the amount of power generated in the solar power systems of Berkeley, California in an accurate, flexible way.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Evidence and Prediction Regarding the Continuous Global Warming: A Severe Domino Effect
AU  - Junhyeop Cho
AU  - Gyeongseung Han
AU  - Christopher Jeongchan Lee
AU  - Suh Hyun Lee
AU  - Aaron Youngwoo Yoo
Y1  - 2021/10/12
PY  - 2021
N1  - https://doi.org/10.11648/j.ajese.20210504.11
DO  - 10.11648/j.ajese.20210504.11
T2  - American Journal of Environmental Science and Engineering
JF  - American Journal of Environmental Science and Engineering
JO  - American Journal of Environmental Science and Engineering
SP  - 76
EP  - 86
PB  - Science Publishing Group
SN  - 2578-7993
UR  - https://doi.org/10.11648/j.ajese.20210504.11
AB  - Global warming has been a major threat to Earth for decades; still, this issue has not been taken seriously by many. Although it is proven that one of its main causes is human activity, humanity’s effort towards a safer, healthier planet has been minimal. After years of neglect, global warming has worsened, and its adverse effects have become more severe. This paper aims to underscore the necessity of human efforts and universal contribution to subside the devastating ramifications of global warming. To investigate the past, present, and possible future consequences of global warming, this paper analyzes data mostly obtained from the United States Environmental Protection Agency (EPA). The paper also presents graphs that clearly illustrate the increases in global sea levels, permafrost temperatures, sea surface temperatures, and concentrations of greenhouse gases. Furthermore, the paper utilizes a linear regression machine learning algorithm, a method widely used by researchers to create predictive models, to depict future trends of the data of the aforementioned subjects. This analysis and visualization of data conclude that a so-called “domino effect” was certainly present as some environmental changes of global warming. To solve the problem of global warming, the paper finally uses the K-neighbor regression method in Python to predict the amount of power generated in the solar power systems of Berkeley, California in an accurate, flexible way.
VL  - 5
IS  - 4
ER  -

Copy | Download