Earthquake Prediction Using Bidirectional Long Short-Term Memory with Optimize Inference Rules and Uncertainty Using Reinforcement Learning
The inherent uncertainty in seismic events, earthquakes prediction is still one of the difficult. A novel hybrid approach is presented to solve this: it is a combination of intelligent rule-based systems and deep learning (DL) architectures. This model uses Bidirectional Long Short-Term Memory (BiLSTM) for temporal sequence modeling. A fuzzy inference system (FIS) helps managing AND DEAL WITH prediction's uncertainty. Using a agent based on Q-learning, the fuzzy rule base is tuned dynamically for the purpose of increasing performance over time. The model has been trained on 11,442earthquake event dataset. Attaching a Mean Absolute Error (MAE) of 0.016, minimum Root Mean Squared Error (RMSE) of 0.021, and a R 2 score of 0.87 explaining 87% of the data variance, experimental findings show performance gains. These findings demonstrate the excellent ability regarding the proposed framework for controlling uncertainty, hence offering valuable information with regard to proactive risk reduction.
