Detecting Extreme Weather Patterns Using AI in Bogor Region
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Abstract
Extreme weather events have become more frequent and intense globally, necessitating advanced monitoring and prediction methods. Bogor, Indonesia, known for its complex weather patterns and high rainfall intensity, faces increasing risks of flooding and landslides. This literature review explores the use of Artificial Intelligence (AI) techniques in detecting and predicting extreme weather patterns, with a focus on the Bogor region. Methods such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Random Forest (RF), and hybrid AI models are analyzed for their effectiveness. Key challenges, including data quality, model scalability, and computational requirements, are also discussed. The study highlights AI's potential to revolutionize weather monitoring and disaster mitigation efforts, emphasizing the need for robust and interpretable models tailored to local conditions.
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