International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Exploring Advanced Techniques in Artificial Intelligence for Environmental Monitoring and Climate Change Management

Author(s) Chalamalla Nikhitha Reddy
Country India
Abstract Environmental monitoring is crucial for addressing climate change impacts, demanding innovative approaches for better prediction and management. This study explores advanced artificial intelligence (AI) techniques beyond traditional models like CNNs and LSTMs. It incorporates generative adversarial networks (GANs) for augmenting sparse datasets, ensemble learning for robust predictions, and explainable AI (XAI) to enhance model transparency and usability. GANs address data scarcity by generating synthetic, high-fidelity environmental data, while transformer-based architectures improve long-term climatic forecasts. Ensemble methods demonstrate superior accuracy in predictions, reducing mean squared error by 15% compared to traditional models. Reinforcement learning (RL) optimizes adaptive climate strategies by analyzing dynamic environments in real-time. These approaches collectively enhance the precision, interpretability, and scalability of AI-driven environmental monitoring systems. Future research should explore federated learning and quantum computing to further advance computational efficiency and accessibility. This study highlights AI's transformative potential in fostering proactive and data-driven climate resilience
Keywords Artificial Intelligence (AI), Environmental Monitoring, Climate Change Management, Generative Adversarial Networks (GANs), Explainable AI (XAI), Ensemble Learning, Reinforcement Learning, Transformer Models
Field Engineering
Published In Volume 7, Issue 1, January-February 2025
Published On 2025-01-04
Cite This Exploring Advanced Techniques in Artificial Intelligence for Environmental Monitoring and Climate Change Management - Chalamalla Nikhitha Reddy - IJFMR Volume 7, Issue 1, January-February 2025. DOI 10.36948/ijfmr.2025.v07i01.34529
DOI https://doi.org/10.36948/ijfmr.2025.v07i01.34529
Short DOI https://doi.org/g82hg3
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