# Large Earthquake Prediction in Chile with Imbalanced Classifiers and Ensemble Learning
## 2 Related Works  
The problem of earthquake prediction, especially big magnitude ones, has generated a great number of approaches, from many different points of view [3]. [PERSON] and [PERSON] [4] presented a method of data-based probabilistic seismic hazard analysis (PSHA) and ground motion simulation, verified using previously recorded strong-motion data and machine-learning techniques. It showed the benefits of applying such methods to strong-motion databases for PSHA and ground motion simulation, particularly in large urban areas, where dense instrumentation is available or expected. The use of artificial neural networks (ANNs) has been extensively proposed in recent years [5]. In particular, the authors in [6] estimated the magnitude of the events recorded daily, showing as the ANNs are a promising technique for earthquake prediction. It is also proved that ANN training on the global data on earthquakes is much more effective for a local earthquake prediction, than an ANN training on local data. Other approaches, like [7], focus on preparing data-sets. In this case, a monitoring system to prepare machine learning data-sets for earthquake prediction based on seismic-acoustic signals is proposed due to the difficulty of making predictions given that kind of data. Using on-line recordings of robust noise monitoring (RNM) signals of anomalous seismic processes (ASP) from stations in Azerbaijan, an Earthquake-Well Signal Monitoring Software has been developed to construct data sets. Another promising approach for the next generation of earthquake early warning system is based on predicting ground motion directly from observed ground motion, without any information of hypocenter. [PERSON] et al. [8] predicted seismic intensity at the target stations from the observed ground motion at adjacent stations, employing two different methods of correction for site amplification factors: scalar correction and frequency-dependent correction prediction, being this last one more accurate. Frequency-dependent correction for site amplification in the time domain may lead to more accurate prediction of ground motion in real time. Multi-step prediction is also present in literature. The authors in [9] proposed a multi-step prediction method of EMD-ELM (empirical mode decomposition-extreme learning machine) to achieve the short-term prediction of strong earthquake ground motions. The predicted results of near-fault acceleration records demonstrate that the EMD-ELM method can realize multi-step prediction of acceleration records with relatively high accuracy. Bayesian networks (BNs) were used as a novel methodology in order to analyze the relationships among the earthquake events in [10]. The authors proposed a way to predict earthquake from a new perspective, constructing a BN after processing, which is derived from the earthquake network based on space-time influence domain. Then, the BN parameters are learnt by using the cases which are designed from the seismic data in the period between 1992 and 2012. At last, predictions are done for the data in the period between 2012 and 2015, combining the BN with the parameters. The results show that the success rate of the prediction including delayed prediction is about 65%. In the context of the imbalanced learning, there are several approaches that have been recently published addressing different problems. [PERSON] et al. [11] presented a method for identifying peptide motifs binding to 14-3-3\(\sigma\) isoform, in which a similarity-based undersampling approach and a SMOTE-like oversampling approach are used to deal with imbalanced distribution of the known peptide motifs, contributing to create a fast and reliable computational method that can be used in peptide-protein binding identification in proteomics research.