An improved genetic algorithm and clone selection optimization-based gated recurrent unit networks for earthquake magnitude prediction

Earthquake magnitude prediction is a vital rendezvous for human safety, economic and property losses. The earthquake occurrence process represents a highly complex nonlinear problem. Meanwhile, artificial intelligence methods have emerged as automated and intelligent frameworks for addressing magnitude prediction challenges. However, these approaches ignore redundant features and have lower prediction accuracy. Genetic Algorithms (GA) excel in feature selection and Gated Recurrent Units (GRU) have strong time series prediction capabilities. Therefore, we propose a novel earthquake magnitude prediction method, named Improved GA and a Clone Selection Optimization-based GRU (IGA-CSOGRU). First, an improved GA with generation gap strategy is presented to enhance the feature selection capability of time-series data in prediction models. Second, GRU is implemented as the core prediction model. To optimize its hyperparameters, a novel approach combining Latin hypercube sampling with adaptive mutation CSO is introduced, thereby enhancing prediction performance. Finally, to validate the performance of the proposed IGA-CSOGRU, a novel earthquake magnitude prediction dataset is constructed, which is acquired from the self-developed Acoustic & Electromagnetics to AI (AETA) platform. Evaluation metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Squared Error (MSE), and $R^2$ were used for assessment. The proposed IGA-CSOGRU model demonstrates significant performance improvements across all datasets, achieving an average RMSE reduction of 5%–7% compared to all baseline methods, highlighting the model’s superior capability in handling challenging time series prediction tasks. The implementation code supporting the findings of this study is available at https://github.com/123fggv/Earthquake-prediction.