Data-driven Modeling as a Revolutionary Technology for Conservation and Environment Management: With Focus on Prediction of Future Global Carbon Dioxide and Methane Levels

Conservation entails the protection of wild flora, fauna, and their natural habitats. The success of conservation and environment management is a global concern as they have faced immense threats. Among these threats, climate change takes the first place as the rapid changes in climate have fuelled rises in greenhouse gases, changes in average rainfall, storm occurrences, and rise in sea levels. The rise in greenhouse gases such as carbon dioxide and methane has been associated with an increase in global temperatures. These gases pose massive impacts to the environment thus drastically reducing the level of their emissions can help in conservation and management of the environment. The use of cutting-edge technology such as machine learning gives hope for present and future conservation and environment management initiatives. Three machine learning algorithms (random forest, deep neural networks, and polynomial regression) were used to forecast global carbon dioxide and methane levels for the next 500 years with 2024 as the baseline year. Deep neural networks performed exceptionally well in forecasting global CO ₂ and CH ₄ levels. These findings prove that data-driven modeling can be utilized in the conservation and management of the environment due to its effectiveness.