The Role of AI in Climate Change Prediction: Analysing Machine Learning Models for Extreme Weather Forecasting

ML and AI are relevant in climate change prediction, more so in extremal weather conditions observation. In terms of ability of the technologies are being applicable in spatial and temporal analysis, estimation, and climatic data. AI models, especially LSTM and YConvLSTM deep learning structures, outperform numerical weather prediction methods. These models are more effective in identifying fragile climate parameters and crossectional patterns. Nonetheless, the LSTM (Long ShortTearm Memory) technology can come in handy in identifying severe weather occasions because of its temporal endurance. YConvLSTM contributes to the enhancement of the regressionbased modeling for the purpose of minimizing the prediction errors, hence enhancing the predictability of the model. The fact is that YConvLSTM helps improve decent predictability of dependability predictions. They help in development of efficient disaster response plans, distribution of resources and even in policy formulation by producing good forecasts. However, rising computational costs, data integrity concerns, and ethical concerns remain. Climate research using artificial intelligence can handle the increasing level of complexity of rising temperatures and extreme weather occurrences. This makes it vital to climate resiliency measures and disaster recovery planning to make people aware of the climate.