# Predicting the Magnitude of Earthquakes Using Grammatical Evolution
## 1 Introduction
Furthermore, they demonstrated its robustness by applying it successfully to independent datasets from Japan, Italy, and New Zealand [19]. A subsequent study aimed to develop a model to predict both the location and magnitude of potential earthquakes occurring in the following week based on seismic data from the current week, focusing on seismogenic regions in southwestern China. The model achieved a testing accuracy of 70%, with corresponding precision, recall, and F1-score values of 63.63%, 93.33%, and 75.66%, respectively [20]. Moreover, an ensuing study focused on predicting earthquake magnitudes in the Hindukush region. Four machine learning techniques--namely, pattern recognition based on neural networks, Recurrent Neural Networks, random forest, and a linear programming boost ensemble classifier--were individually implemented to model the relationships between computed seismic parameters and the occurrence of future earthquakes [21]. Additionally, another study demonstrated that machine learning can effectively predict the timing and size of laboratory earthquakes by reconstructing and making sense of the system's intricate spatiotemporal loading history [22]. In a related study, a machine learning technique was applied to the regions of Japan, Turkey, Greece, and the Indian subcontinent. The model revealed a relationship between the computed seismic data and the occurrence of future earthquakes [23]. Another paper compared the performance of a machine learning model which uses a limited set of predictor variables--surface roughness, peak frequency (fP), HV, VS30, and depth (Z2.5)--to that of a physics-based model (GRA) relying on detailed 1D velocity profiles. The results indicated that the machine learning approach outperformed the physics-based modeling in terms of predictive accuracy [24]. Another study investigated physical and dynamic variations in seismic data, and introduced a novel machine learning method called Inverse Boosting Pruning Trees (IBPT). This approach was designed to provide short-term forecasts based on satellite data from 1371 earthquakes with a magnitude of six or higher, given their significant environmental impact [25]. In contemporary scientific research, there is a growing surge of interest in the prediction of seismic events. Advancements in data acquisition technologies, communication networks, edge-cloud computing, the Internet of Things (IoT), and big data analytics have created favorable conditions for the development of intelligent earthquake prediction models, enabling early-warning systems in vulnerable regions [26]. In this context, it is noteworthy that in recent years, there has been significant attention paid to the development of various models for the early detection of seismic events, which are now accessible to the general public through mobile devices in the form of applications (apps), TV media, or radio. For instance, in Japan, significant investment has been made in the timely dissemination of information regarding seismic events since 2007, through the implementation of the Earthquake Early Warning (EEW) system [27]. This enables an alert for an impending earthquake to be issued from up to one minute to several seconds before the event occurs [28]. Moreover, the United States has also developed its own earthquake warning system through the U.S. Geological Survey. Since 2016, a system known as Shake Alert has been implemented for the West Coast [29; 30]. In Southern Europe, specifically at the University of Naples in Italy, a software model known as PRESTo has been developed, which is capable of detecting earthquakes approximately within the last ten seconds before their occurrence. It is abundantly clear that the science of machine learning has entered the field of seismology with considerable momentum, a fact that is also reflected in the bibliographic references, with two articles authored by specialists in the fields of geological and geophysical sciences highlighting how machine learning contributes to the advancement of earthquake prediction.