# Application of Machine Learning Models to Multi-Parameter Maximum Magnitude Prediction
## 1 Introduction  
Earthquakes are natural phenomena resulting from the release of energy within the Earth. Earthququakes with a magnitude above five often lead to casualties and damage to infrastructure, posing long-term challenges and impacts on socio-economic structures. This impact is especially significant in earthquake-prone regions, such as the North-South Seismic Belt in China. Therefore, improving the accuracy of earthquake predictions and implementing timely and effective disaster prevention and mitigation measures have become critical issues that urgently need to be addressed in earthquake science. Researchers have explored and applied various earthquake prediction methods in the past. These methods include observing and recording unusual phenomena in animal behavior [1, 2], analyzing the potential impact of changes in Earth's internal composition on seismic activity [3], monitoring abnormal fluctuations in radon emissions [4, 5, 6], tracking anomalies in pre-earthquake thermal infrared radiation [7, 8, 9], detecting possible electromagnetic precursors before earthquakes [10, 11], monitoring changes in the speed of seismic wave propagation [12, 13], and identifying subtle deformations in the Earth's crust [14, 15, 16]. These methods often rely on historical earthquake data and statistical models to reveal certain statistical patterns for earthquakes, such as predictions of earthquake frequency and magnitude distribution. However, significant challenges remain regarding specific timeand location predictions. First, the occurrence time of earthquakes has a high degree of uncertainty. Although statistical methods can estimate the probability of large earthquakes occurring in a certain region, accurately predicting the timing of an earthquake remains nearly impossible. Earthquakes often have no obvious precursors, and existing statistical models usually only provide long-term probability estimates, making short-term predictions difficult. Secondly, spatial predictions of earthquakes are also challenging. Traditional methods generally estimate the spatial distribution of potential earthquakes by analyzing historical earthquake records, but the spatial distribution of seismic activity is often influenced by complex geological factors such as crustal movements, stress concentrations, and fault activities. As a result, traditional methods are limited in providing precise predictions on smaller spatial scales. With the advancement of technology and algorithms, machine learning methods have gradually emerged as one of the most promising approaches in earthquake prediction. With powerful data processing and learning capabilities, machine learning methods can deeply explore the complex signals and patterns that precede earthquakes and are expected to enable the prediction of earthquake magnitude, time, and location, which provides valuable information for earthquake early warning and disaster assessment studies. [PERSON] et al. (2017) applied the backpropagation (BP) neural network model to earthquake prediction in China. They used indicators such as earthquake frequency, maximum magnitude, and mean magnitude as inputs, with the maximum magnitude of the next earthquake as the output. The model accurately predicted five out of eight magnitude samples, achieving an accuracy rate of over 60%. [PERSON] and [PERSON] (2018) proposed two neural network-based methods to estimate the location and timing of future moderate to large earthquakes. The first method divides large earthquake regions into smaller areas and splits historical earthquake records into equal time intervals. It calculates seismic activity indicators for each subregion within each time interval and uses a recurrent neural network to study the relationship between these indicators and the maximum magnitude in the subsequent time interval. The second method directly divides the historical earthquake records into unequal time intervals.