Environmental Sustainability in the Age of Deep Learning: Balancing Technological Advancement with Ecological Responsibility

Abstract

The convergence of technological innovation, particularly deep learning (DL), with the importance of responsibility for the environment in achieving environmental sustainability. Deep learning (DL) offers to improve sustainability in different areas. This paper discusses DL breakthroughs and their applications in accomplishing SDGs, renewable energy, and environmental health. This discovers problems in reconciling technological innovation with caring for the environment by investigating the uses of deep learning in diverse areas and measuring their environmental implications. Furthermore, it explores CNN and LSTM techniques in Deep learning for incorporating environmental factors into the development, application, benefits and challenges of DL technologies to promote sustainability. This study aims to provide insights and recommendations for creating a harmonious link between technical advancement and ecological responsibility in the pursuit of environmental sustainability by conducting a comprehensive review of existing literature. There are three indicators: MAPE, RMSE, and MAE. The MAPE, RMSE, and MAE results are provided based on 7.5, 15, and 30 minutes, indicating low forecast accuracy.