ML and AI to Predict Climatic Conditions

AI-driven system developed to predict the effects of climate change by leveraging advanced machine learning techniques. By integrating diverse datasets including atmospheric measurements, oceanic data, satellite imagery, and socioeconomic indicators, the system aims to uncover complex relationships influencing climate dynamics. Machine learning algorithms such as regression, classification, and clustering are applied to analyze and predict climate patterns and trends. Deep learning models such as CNNs and RNNs are utilized for time-series analysis and forecasting, enhancing the system's ability to provide accurate predictions. Ultimately, this AI-based approach seeks to support informed decision-making and adaptive strategies in mitigating the impacts of climate change on ecosystems, agriculture, water resources, and human health.

Authors

Publication Details

Journal
International Journal for Research in Applied Science and Engineering Technology
Published
2024-11-07
DOI
https://doi.org/10.22214/ijraset.2024.64876
Citations
3
Primary Topic
Neural Networks and Applications
Type
article
Field-Weighted Citation Impact
1.04
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article

ML and AI to Predict Climatic Conditions

Mr. G. Vimalsubramanian
3 citations
International Journal for Research in Applied Science and Engineering Technology
Neural Networks and Applications
1.04
article

ML and AI to Predict Climatic Conditions

Mr. G. Vimalsubramanian
article en
3 citations

Abstract

AI-driven system developed to predict the effects of climate change by leveraging advanced machine learning techniques. By integrating diverse datasets including atmospheric measurements, oceanic data, satellite imagery, and socioeconomic indicators, the system aims to uncover complex relationships influencing climate dynamics. Machine learning algorithms such as regression, classification, and clustering are applied to analyze and predict climate patterns and trends. Deep learning models such as CNNs and RNNs are utilized for time-series analysis and forecasting, enhancing the system's ability to provide accurate predictions. Ultimately, this AI-based approach seeks to support informed decision-making and adaptive strategies in mitigating the impacts of climate change on ecosystems, agriculture, water resources, and human health.

International Journal for Research in Applied Science and Engineering TechnologyVol. 12(11)
Openalex Percentile: Top 18%
Neural Networks and Applications
1.04
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