Advancing Environmental Monitoring through AI: Applications of R and Python
<p id="p1">The integration of Large Language Models (LLMs), artificial intelligence (AI), and programming languages such as Python and R has revolutionized environmental monitoring. These technologies enhance data analysis, automate reporting, and improve communication among stakeholders, enabling m...
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| Autores principales: | , , |
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| Formato: | Capítulo de libro |
| Lenguaje: | inglés |
| Publicado: |
IntechOpen
2025
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| Materias: | |
| Acceso en línea: | https://www.intechopen.com/chapters/89771 |
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| Sumario: | <p id="p1">The integration of Large Language Models (LLMs), artificial intelligence (AI), and programming languages such as Python and R has revolutionized environmental monitoring. These technologies enhance data analysis, automate reporting, and improve communication among stakeholders, enabling more informed and timely decision-making. AI-driven tools facilitate a wide range of environmental monitoring activities, including pollution tracking, species conservation, and climate change analysis, by increasing the accuracy and speed of data processing. The predictive capabilities of AI are essential for forecasting environmental conditions and trends, supporting the development of effective policies and actions. Additionally, AI aids in regulatory compliance by continuously monitoring and analyzing real-time data, alerting authorities to potential violations. Community engagement is also enhanced as AI makes environmental data accessible and understandable, fostering greater public awareness and participation in conservation efforts. Despite these advancements, challenges such as data privacy, model bias, interpretability, and data quality must be addressed to fully leverage the potential of these technologies. As AI, Python, and R continue to evolve, their applications in environmental sciences are expected to significantly contribute to sustainable development and conservation efforts globally.</p> |
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| ISBN: | 978-0-85014-228-0 |
| DOI: | 10.5772/intechopen.1007683 |