Artificial Intelligence-Based Climate Monitoring and Prediction Models

Min-Jae Park

Artificial Intelligence-Based Climate Monitoring and Prediction Models

Author(s)	Dr. Min-Jae Park
Country	Korea (South)
Abstract	Climate change has increased the importance of accurate environmental monitoring and reliable prediction systems for understanding atmospheric, terrestrial, and oceanic processes. Conventional climate-analysis approaches can require extensive computational resources and may face challenges in processing increasingly large and complex environmental datasets. Artificial intelligence (AI), machine learning, deep learning, remote sensing, and advanced data analytics provide new opportunities for improving climate monitoring and prediction. This study examines AI-based climate monitoring and prediction models, focusing on environmental data processing, climate forecasting, extreme-event prediction, remote sensing, anomaly detection, and intelligent climate-risk assessment. A qualitative descriptive and conceptual research design was adopted using secondary academic literature. The analysis indicates that AI models can process large-scale climate datasets and identify nonlinear relationships among atmospheric and environmental variables. Machine-learning algorithms can support temperature, precipitation, drought, wildfire, and extreme-weather prediction, while deep-learning approaches can analyze satellite imagery and other high-dimensional observations. AI-based monitoring systems can also identify environmental anomalies and provide near-real-time information for climate-risk management. However, challenges include data quality, model interpretability, computational requirements, uncertainty, geographical bias, limited historical observations, and difficulties in transferring models across climate regions. Effective AI-based climate prediction requires integration of observational data, physical climate knowledge, advanced computational methods, and transparent validation procedures. Future research should investigate physics-informed machine learning, explainable AI, multimodal climate models, digital twins, high-resolution regional prediction, and uncertainty-aware forecasting. AI-based climate monitoring and prediction can strengthen climate information systems when advanced computational methods are combined with reliable environmental observations and established scientific principles.
Keywords	Artificial Intelligence, Climate Monitoring, Climate Prediction, Machine Learning, Deep Learning, Climate Change, Remote Sensing, Environmental Data Analytics, Extreme Events, Climate Modeling
Field	Engineering
Published In	Volume 8, Issue 3, May-June 2026
Published On	2026-05-10

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American International Journal of Advanced Research and Development

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Artificial Intelligence-Based Climate Monitoring and Prediction Models

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