Journal
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
Volume 29, Issue 12, Pages 17346-17357Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s11356-021-16960-2
Keywords
Neural network; Time series forecasting; Gaussian Process Regression; Holt's method; CO2 emission
Categories
Ask authors/readers for more resources
Global warming and climate change have become one of the most embarrassing and explosive problems/challenges worldwide, especially in third-world countries. Different methods were applied to forecast CO2 emissions in Bahrain, with the neural network model performing better in this case.
Global warming and climate change have become one of the most embarrassing and explosive problems/challenges all over the world, especially in third-world countries. It is due to a rapid increase in industrialization and urbanization process that has given the boost to the volume of greenhouse gases (GHGs) emissions. In this regard, carbon dioxide (CO2) is considered a significant driver of GHGs and is the major contributing factor for global warming. Considering the goal of mitigating environmental pollution, this research has applied multiple methods such as neural network time series nonlinear autoregressive, Gaussian Process Regression, and Holt's methods for forecasting CO2 emission. It attempts to forecast the CO2 emission of Bahrain. These methods are evaluated for performance. The neural network model has the root mean square errors (RMSE) of merely 0.206, while the Gaussian Process Regression Rational Quadratic (GPR-RQ) Model has RMSE of 1.0171, and Holt's method has RMSE of 1.4096. Therefore, it can be concluded that the neural network time series nonlinear autoregressive model has performed better for forecasting the CO2 emission in the case of Bahrain.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available