Analysis of the Influence of Artificial Intelligence on Climate Change Surveillance
Year: 2025
Type: article
Abstract: The phenomenon of climate change emerges as a significant concern of the 21st century, wielding substantial implications for ecosystems, human populations, and economic stability. Effective monitoring plays an indispensable role in addressing these challenges by facilitating the identification of trends and providing the necessary data to inform mitigation and adaptation strategies. Traditional climate monitoring methodologies often exhibit limitations, including high costs, slow data processing, and insufficient resolution, which hinder timely responses to rapid environmental changes. In this context, Artificial Intelligence (AI) has assumed a crucial role in overcoming these challenges. The capabilities of AI in processing extensive datasets, recognizing patterns, and making predictions enhance the accuracy and efficiency of climate change surveillance at unprecedented rates. This study evaluates various AI technologies that support climate change monitoring, including machine learning, deep learning, natural language processing, and big data analytics. The analysis encompasses applications in climate forecasting, satellite observation, wildfire detection, energy management, and carbon emission tracking. The study further discusses the benefits of AI, such as improved data processing, real-time monitoring, and predictive abilities, while acknowledging the challenges of data integrity, integration, scalability, and ethical considerations. The paper concludes by speculating on future advancements in AI, including ecosystem modelling, AI-enhanced climate policy formulation, and collaborative AI networks, which hold the potential to significantly bolster global initiatives aimed at promoting climate resilience and sustainability.
Authors: Dr Amit Bijon Dutta -, Dr. Ipshita Sengupta -
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: hybrid
OpenAlex ID: W4408184482