AI-Driven greenhouse gas monitoring: enhancing accuracy, efficiency, and real-time emissions tracking

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Abstract

This research paper introduced a groundbreaking approach to greenhouse gas (GHG) monitoring by leveraging artificial intelligence (AI) technology to enhance both accuracy and efficiency. Traditional GHG monitoring methods are often hampered by high costs, labor-intensive processes, and significant delays in data analysis. This study harnessed advanced AI techniques, such as machine learning algorithms and neural networks, to facilitate real-time data collection and analysis from diverse sources including satellite imagery, Internet of Things (IoT) sensors, and atmospheric models. By implementing AI models like random forest, support vector machines, convolutional neural networks (CNNs), and long short-term memory (LSTM) networks, the research achieved substantial improvements such as reducing data reporting latency from 24 hours to just 1 hour, increasing spatial resolution from 30 meters to 10 meters, and enhancing detection accuracy from 80% to 95%. Additionally, the AI systems identified previously unknown emission sources and accurately forecasted future emission trends with a high correlation (R²=0.89). These advancements not only allow for more precise identification of emission hotspots and tracking of changes over time but also facilitate more effective regulatory responses and policy-making. The findings underscore the transformative potential of AI-driven monitoring systems in bolstering global sustainability efforts and combating climate change.

Keywords

artificial intelligence / greenhouse gas monitoring / traditional greenhouse gas monitoring / AI-driven monitoring systems / accuracy / efficiency

CHANGE HISTORY

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