| _version_ | 1866901163723456512 |
|---|---|
| author | Trivikram Sai Krovi Ashika S S Jenifer Shanmugasundaram Sushmithaasri K N |
| author_facet | Trivikram Sai Krovi Ashika S S Jenifer Shanmugasundaram Sushmithaasri K N |
| contents | Climate change poses one of the most significant challenges of our time, affecting ecosystems, human health, and economies globally. The increasing concentration of greenhouse gases, particularly carbon dioxide (CO2), has led to unprecedented global warming and climate disruptions. To combat these effects, it is imperative to develop innovative strategies that not only reduce emissions but also enhance our ability to adapt to changing climate conditions. Artificial intelligence (AI) has emerged as a powerful tool in this endeavor, offering advanced capabilities in data analysis, predictive modeling, and real-time monitoring. This study presents a comprehensive analysis of historical carbon dioxide (CO2) levels using a dataset comprising monthly average CO2 mole fractions from March 1958 to the present. A Seasonal Autoregressive Integrated Moving Average with Exogenous Factors (SARIMAX) model was employed to forecast future CO2 levels. The SARIMAX model's suitability for capturing seasonal variations and trends in time series data was leveraged to make accurate predictions. This research highlights the importance of historical data analysis in understanding and predicting CO2 trends, contributing valuable insights for climate change studies and policy-making. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18137397 |
| institution | Zenodo |
| language | |
| publishDate | 2024 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Forecasting Future CO2 Levels (Ppm) using the SARIMAX Model Trivikram Sai Krovi Ashika S S Jenifer Shanmugasundaram Sushmithaasri K N Artificial Intelligence SARIMAX model CO2 levels climate change predictive modeling real-time monitoring Climate change poses one of the most significant challenges of our time, affecting ecosystems, human health, and economies globally. The increasing concentration of greenhouse gases, particularly carbon dioxide (CO2), has led to unprecedented global warming and climate disruptions. To combat these effects, it is imperative to develop innovative strategies that not only reduce emissions but also enhance our ability to adapt to changing climate conditions. Artificial intelligence (AI) has emerged as a powerful tool in this endeavor, offering advanced capabilities in data analysis, predictive modeling, and real-time monitoring. This study presents a comprehensive analysis of historical carbon dioxide (CO2) levels using a dataset comprising monthly average CO2 mole fractions from March 1958 to the present. A Seasonal Autoregressive Integrated Moving Average with Exogenous Factors (SARIMAX) model was employed to forecast future CO2 levels. The SARIMAX model's suitability for capturing seasonal variations and trends in time series data was leveraged to make accurate predictions. This research highlights the importance of historical data analysis in understanding and predicting CO2 trends, contributing valuable insights for climate change studies and policy-making. |
| title | Forecasting Future CO2 Levels (Ppm) using the SARIMAX Model |
| topic | Artificial Intelligence SARIMAX model CO2 levels climate change predictive modeling real-time monitoring |
| url | https://doi.org/10.5281/zenodo.18137397 |