## 1 Introduction  
Earthquakes are considered one of the most dangerous natural disasters, as they can occur without warning. The ratio of deaths caused by earthquakes is above half more than that of other natural disasters [1]. According to the World Health Organization (WHO), earthquakes killed 750,000 people worldwide between 1998 and 2017 [1]. During this period, more than 125 million people were affected by tremors, meaning that they were either injured or lost their houses and valuable properties. In 2020, Americans lost USD 4.4 billion, due to catastrophic earthquakes. Seismic activity prediction is the optimal technique for avoiding earthquake-related economic and human tragies. Machine learning (ML) approaches play a pivotal role in prediction and forecasting in various fields, including different disasters, such as floods, earthquakes, and landslides [2, 3, 4, 5, 6, 7, 8]. Significant research has been conducted, using these techniques, to reduce the impact of the aforementioned disasters [3, 6, 7, 9]. These studies have utilized a variety of machine learning approaches, including artificial neural network [5], support vector machine [9], random forest [10], and convolutional neural network [6]. In this work, we consider the seismic activity prediction problem as a binary classification problem, and present a deep neural network model for predicting the occurrence or otherwise of significant seismic activity. [PERSON] et al. [11] used genetic programming and Ada-boost methods to classify seismic activities in the California region. The authors applied the mentioned methods on only one dataset, and reported accuracy of 78%. An artificial neural network was implemented by [PERSON] et al. [5] for earthquake prediction in the Indonesian region, and it calculated the mean square error. [PERSON] et al. [6] studied the Palu region in Indonesia, to identify earthquake-prone areas using cluster analysis techniques. The authors used silhouette clustering, pure locational clustering based on hierarchical clustering analysis, and convolutional neural networks. The approach to the selected region achieved 89% accuracy. [PERSON] et al. [12] used a moth flame optimized functional link with an artificial neural network to predict seismic magnitude on earthquake catalog data, by considering the mean square error as a metric. [PERSON] et al. [13] discussed the precursory pattern-based feature extraction method for earthquake prediction in China. The authors used an artificial neural network for earthquake prediction, and reported accuracy of 80%. A different approach was used by [PERSON] et al. [14] in the northern areas of Pakistan, for the prediction of seismic activity. The authors implemented the support vector machine and hybrid neural network on the targeted area, to predict earthquake occurrence for a period of one month: the maximum accuracy of their models was 79% on one dataset. [PERSON] et al. [15] advocated the use of long short-term memory network structure for predicting earthquakes in the Bangladesh region: the authors used hyper-parameters optimization, as well as L1 and L2 regularization, to achieve maximum accuracy of 76%. From the current literature, we identified that although machine learning models are used to predict earthquake occurrence to varying degree of success, the models mostly rely on data from one region, and there is no generalization in the proposed models. Generalization refers to the concept of the effectiveness (such as higher accuracy, low mean squared error) of a given machine learning model at learning from the given data, and effectively applying the learning to other datasets. The machine learning models proposed for earthquake prediction are not generalized, i.e., the proposed models performed well (to a certain degree) on the given datasets, but their performances on other datasets were either not evaluated or were found lacking: that is to say, the models could not be applied to other datasets/regions.