Intelligent Earth: AI As A Catalyst For Climate Action - Mr. Achinta Kumar Palit; Mr. Abhishek Panda (International Journal of Environmental Noise and Pollution Control, 2026)
Artificial Intelligence (AI) is assuming an increasingly influential role in climate science, providing advanced tools capable of interpreting vast, complex, and multi-dimensional environmental datasets. Traditional climate modeling approaches, while grounded in physical principles, frequently struggle to deliver high-resolution, real-time, and region-specific forecasts because of heavy computational demands, incomplete observations, and uncertainties in representing small -- scale processes. Artificial intelligence (AI) techniques, especially machine learning and deep learning, provide strong substitutes and enhancements to these approaches. By identifying hidden patterns, nonlinear relationships, and trends within massive streams of data, AI supports the development of hybrid frameworks that blend empirical observations with physics -- based simulations, ultimately improving predictive precision and spatial granularity. The fusion of AI with satellite remote sensing technologies has significantly strengthened the capacity for continuous, automated monitoring of environmental change. These systems can rapidly detect and quantify phenomena such as deforestation, glacial retreat, biodiversity loss, urban heat island effects, air pollution dynamics, and fluctuations in sea surface temperatures. In addition, AI -- driven forecasting and early warning platforms are enhancing preparedness for extreme weather events, including hurricanes, floods, droughts, and wildfires, enabling quicker response times and more informed disaster risk management strategies. Despite its transformative promise, integrating AI into climate research is not without obstacles. Persistent issues related to data quality, representativeness, algorithmic bias, interpretability, and reproducibility require careful attention. Effective progress also depends on strong cross-disciplinary collaboration among climate scientists, computer engineers, policymakers, and local stakeholders. Ethical considerations, transparency in model development, and equitable access to technological benefits remain central to responsible deployment. As the scale and urgency of climate challenges intensify, AI is emerging as a critical enabler for evidence-based policymaking, efficient resource allocation, and long -- term sustainability planning, reshaping how humanity understands and responds to a rapidly changing planet.
Keywords: Artificial Intelligence (AI); Deep Learning; Environmental monitoring; Machine Learning; remote sensing