Large Earthquake Magnitude Prediction in Chile with Imbalanced Classifiers and Ensemble Learning

Abstract

This work presents a novel methodology to predict large magnitude earthquakes with horizon of prediction of five days. For the first time, imbalanced classification techniques are applied in this field by attempting to deal with the infrequent occurrence of such events. So far, classical classifiers were not able to properly mine these kind of datasets and, for this reason, most of the methods reported in the literature were only focused on moderate magnitude prediction. As an additional step, outputs from different algorithms are combined by applying ensemble learning. Since false positives are quite undesirable in this field, due to the social impact that they might cause, ensembles have been designed in order to reduce these situations. The methodology has been tested on different cities of Chile, showing very promising results in terms of accuracy.

Earthquake Prediction Using Expert Systems: A Systematic Mapping Study

“…Fuzzy logic is not a logic that is fuzzy itself, but a logic that can be used to demonstrate fuzziness [21]. The vagueness of fuzzy logic has been highlighted in [10] by examining the events that cannot be recorded statistically such as crack in the undergroundfault, etc. A new attenuation relationship has been proposed in [8] using three fuzzy input sets including epicentral distance, earthquake magnitude and intensity using earthquake data set of Taiwan and United states of America (USA).…”

“…These tools and techniques have been summarized in Table 9. Annealing, Sparsespike 1.8 [25] Classification and regression trees(CART) 1.8 [49] Fuzzy C-mean 4 [28,77] Upgraded IF THEN ELSE 4 [27,83] Normalized fuzzy peak ground acceleration (FPGA) 1.8 [8] Aeronautical reconnaissance coverage Geographic information system (ARC/INFO GIS) 1.8 [84] Geographic information system (GIS), Multi criteria decision analysis (MCDA) 4 [15,82] Multilayer Preceptron -Rule Based (MLP-RB) 1.8 [21] Nearest neighbor Invariant Riemannian metric (AIRM) 1.8 [52] WI (Weighted index) 1.8 [5] Knowledge extraction based on evolutionary learning (KEEL) 1.8 [10] Particle SWARM Optimization (PSO) 1.8 [56] Apache SPARK 1.8 [59] Kernal Fisher Discriminant Algoritthm (KFDA) 1.8 [60] Novel earthquake early warning system (NEEWS) 1.8 [64] Accuracy of results obtained through the proposed expert system for making earthquake predictions using a training set (TS) or independent test set (ITS) has been listed in Table 10.…”

“…The literature survey reveals that different approaches of expert systems including fuzzy, rule-based, neuro-fuzzyand machine/deep learning methods have been used to forecast future earthquake from historic and instrumental data. In practice, ES have also been efficiently used for risk analysis and assessment in multiple areas such as, information technology (IT) [10], engineering [11], economics, healthcare [12], and civil engineering [13][14][15]. The motivation behind applying expert system technique for earthquake prediction lies in its noticeable effectiveness and reliability [16] of such approaches in other disciplines.…”

Identifying Collapsed Buildings Using Post-Earthquake Satellite Imagery and Convolutional Neural Networks: A Case Study of the 2010 Haiti Earthquake

“…This is a disadvantage and undesirable for rapid damage assessment. It is demonstrated that the number of false positives could be reduced by taking advantage of ensemble learning [65]. However, it is still a challenge to properly deal with an imbalanced dataset [57].…”

Applied Engineering Using Schumann Resonance for Earthquakes Monitoring

“…Figure 5 shows the final results. It is worth mentioning that annually, there are about one million earthquakes of M = 2.0 [83]. The WinkelTripel projection has been used to build the map.…”