Earthquake Forecasting Using CNN-BiLSTM : Integrating Fuzzy Logic and Reinforcement Learning for Uncertainty Optimization

Due mostly to 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, and Convolutional Neural Networks (CNN), for spatial pattern recognition. A fuzzy inference system (FIS) helps managing 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 11,442-earthquake event dataset. Attaching a Mean Absolute Error (MAE) of 0.014, minimum Root Mean Squared Error (RMSE) of 0.019, and a R2 score of 0.89 explaining 89% 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.