Advancing Environmental Sustainability with Artificial Intelligence : Harnessing the Potential of Machine Learning for Climate Change Mitigation - Albert C; Sree Vinesh; Raja Vignesh (International Journal of Scientific Research in Mechanical and Materials Engineering, 2024)
Climate change due to human activity will bring more frequent and intense weather events, greater sea levels, shorter agricultural seasons, and a decrease in biodiversity. A branch of AI known as Machine Learning (ML) is investigated here for its potential to aid in the battle against this problem. Through demand forecasting and optimal placement of solar panels and wind turbines, machine learning enhances renewable energy systems. With its support for smart networks and integration of electric cars, a more sustainable energy future is within grasp. Among machine learning’s many environmentally friendly applications are the preservation of forests, the improvement of irrigation precision, and the automation of trash sorting, all of which contribute to better resource management. Better weather predictions, early warning systems for severe weather, identification of at-risk locations, and corporate sustainability are all possible thanks to machine learning. The fight against climate change requires a transformation to renewable energy sources, more efficient energy usage, new regulations (such carbon pricing), and adaptation to the unavoidable effects. Advancements in several fields may be accelerated with the help of AI. Optimal use of AI for climate action requires a deeper dive into the specific uses of AI for adaptation and mitigation in this article, which examines the challenges and opportunities for collaboration in this area.