AI for Climate Change: Leveraging Machine Learning for Environmental Monitoring and Sustainability

Abstract

As more climate change challenges emerge, AI and ML offer innovative methods for monitoring the environment and sustainable management. This paper aims at discussing the use of Artificial Intelligence technologies including Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNNs), and Random Forest models to solve global environmental challenges. With the help of secondary data, this research assesses AI critically in terms of its applicability for temperature anomaly prediction, deforestation pattern identification, and carbon capture enhancement in various ecosystems. The findings indicate that AI models are more effective than current monitoring systems in terms of both precision and capacity, which can be valuable information for policymakers and environmentalists. Nevertheless, questions like data quality, model interpretability and model robustness are still unsolved and should be focused in subsequent research topics concerning the feasibility of sustainable AI solutions and multidisciplinary cooperation. This paper also shows how AI can be utilized to enhance climate change and improve the world for the better for us and future generations.