Optimization strategies for enhanced disaster management

As a natural disaster, earthquakes pose a significant threat to human life, infrastructure, and societal stability. To mitigate these risks, earthquake forecasting has the potential to provide timely warnings and enable preparedness measures to be taken. Non-seismic activity is dynamic and nonlinear, making earthquake prediction challenging. This study attempts to create a framework for earthquake forecasting that takes into account the dynamic and nonlinear nature of non-seismic activity. The research aims to make detailed earthquake predictions by combining location and date data. Preprocessing techniques such as label encoding and missing-value imputation will preserve the integrity of critical temporal patterns required for accurate forecasting. Preprocessed data can also be utilized to pick the most relevant features via an optimization-based feature selection technique. To achieve maximal performance and effectively capture complex patterns, LSTM model elements such as regularization strength and hyperparameter values must be optimally tuned. By calibrating models to specific dataset properties, this optimization strategy greatly improves forecast accuracy. LSTM modeling and embedded optimization will also be employed to increase computer efficiency and capture significant seismic activity patterns. This platform will be thoroughly tested and assessed using current earthquake datasets, yielding insights into machine learning and optimization approaches for disaster mitigation and preparedness. Proposed RassoNet Optimization approaches using LSTM model has been used to improve the model's performance, resulting in more exact and current earthquake forecasting which has been evaluated using various metrics(R2 score: 0.93, MSE: 0.07, RMSE: 0.26). The framework improves the ability to predict and mitigate seismic occurrences, reducing the risk to people and infrastructure.