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Application of Artificial Intelligence in Predicting Earthquakes: State-Of-The-Art and Future Challenges

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Application of Artificial Intelligence in Predicting Earthquakes: State-Of-The-Art and Future Challenges Received September 16, 2020, accepted September 30, 2020. Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000. Digital Object Identifier 10.1109/ACCESS.2020.3029859 Application of Artificial Intelligence in Predicting Earthquakes: State-of-the-Art and Future Challenges MD. HASAN AL BANNA1, (Associate Member, IEEE), KAZI ABU TAHER1, M. SHAMIM KAISER 2, (Senior Member, IEEE), MUFTI MAHMUD 3, (Senior Member, IEEE), MD. SAZZADUR RAHMAN2, (Member, IEEE), A. S. M. SANWAR HOSEN 4, (Member, IEEE), AND GI HWAN CHO 4, (Member, IEEE) 1Department of Information and Communication Technology, Bangladesh University of Professionals, Dhaka 1216, Bangladesh 2Institute of Information Technology, Jahangirnagar University, Dhaka 1342, Bangladesh 3Department of Computer Science, Nottingham Trent University, Nottingham NG11 8NS, U.K. 4Division of Computer Science and Engineering, Jeonbuk National University, Jeonju 54896, South Korea Corresponding authors: Mufti Mahmud ([email protected]; [email protected]) and Gi Hwan Cho ([email protected]) This work was supported in part by fellowship number 19FS12048 from the Information and Communication Technology Division of the Government of the People's Republic of Bangladesh. ABSTRACT Predicting the time, location and magnitude of an earthquake is a challenging job as an earthquake does not show specific patterns resulting in inaccurate predictions. Techniques based on Artificial Intelligence (AI) are well known for their capability to find hidden patterns in data. In the case of earthquake prediction, these models also produce a promising outcome. This work systematically explores the contributions made to date in earthquake prediction using AI-based techniques. A total of 84 scientific research papers, which reported the use of AI-based techniques in earthquake prediction, have been selected from different academic databases. These studies include a range of AI techniques including rule-based methods, shallow machine learning and deep learning algorithms. Covering all existing AI-based techniques in earthquake prediction, this article provides an account of the available methodologies and a comparative analysis of their performances. The performance comparison has been reported from the perspective of used datasets and evaluation metrics. Furthermore, using comparative analysis of performances the paper aims to facilitate the selection of appropriate techniques for earthquake prediction. Towards the end, it outlines some open challenges and potential research directions in the field. INDEX TERMS AI, deep learning, earthquake, machine learning, review. NOMENCLATURE DL deep learning. DNN deep neural network. ACC ant-colony clustering. DT decision tree. AdaBoost/LPBoost adaptive/linear programming boost. KNN K-nearest neighbors. AE absolute error. ELM extreme learning machine. AHC agglomerative hierarchical cluster- FAR false alarm ratio. ing. FLANN functional link artificial neural network. AI artificial intelligence. FNN fuzzy neural network. ANFIS/FIS adaptive-network-based/fuzzy infer- FUM fuzzy user model. ence system. GA genetic algorithm. ANN artificial neural network. GBV/PBV global/personal best value. AUC area under the curve. GFCV generalized fuzzy clustering variety. BP backpropagation. GLM generalized linear model. The associate editor coordinating the review of this manuscript and GMDH group method of data handling. approving it for publication was Zhiwei Gao . GP grid partitioning. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ VOLUME 8, 2020 1 M. H. A. Banna et al.: Application of AI in Predicting Earthquakes: State-of-the-Art and Future Challenges HWT Haar wavelet transformation. some patterns in locations of earthquakes (denoted by red dots IABC improved artificial bee colony. in Fig. 1). This kind of patterns may provide researchers with IASPEI international association of seismology and possibilities to accurately predict earthquakes. physics of the earth's interior. Earthquake prediction can be classified into the short-term LM Levenberg-Marquardt. and long-term process. Short-term prediction is very compli- LR logistic regression. cated as it predicts earthquakes within days or weeks of their LSTM long short-term memory. occurrences. Therefore, it should be precise and accurate, MAE mean absolute error. and fewer false alarms are appreciated. Generally, short-term MFO moth flame optimization. predictions are used for evacuation of an area before an ML machine learning. earthquake. On the other hand, long-term earthquakes are pre- MLP multi-layer perceptron. dicted based on earthquakes periodical arrival, which carries MSE mean squared error. a few pieces of information. Still, they can help to set stan- NARX nonlinear auto-regressive networks with exoge- dards for building code and designing disaster response plans. nous input. In 2009, L'Aquila city of Italy was struck by a 5.9 magnitude NB Naive Bayes. earthquake, taking away the life of 308 citizens. However, NDAP neural dynamic optimization of Adeli and Park. the earthquake forecast commission of Italy predicted that NDC neural dynamic classification. there would be no damage, and they did not evacuate the NFS neuro-fuzzy system. city. Such faulty prediction can lead to a massive massacre PCA principal component analysis. taking away lives and damaging lots of infrastructures. The PDF probability density function. scientists involved in that incident were punished with six PHMM poisson hidden Markov model. years of imprisonment [2]. PNN probabilistic neural network. The earthquake prediction models perform well with earth- PR polynomial regression. quakes having medium magnitudes, but while the shocks PRNN pattern recognition neural network. have high magnitude, the outcomes achieved are poor. Major PSO particle swarm optimization. earthquakes cause most damages and bring the most con- RBFNN radial basis function neural network. cern. The reason behind this scenario is that there is a RE relative error. smaller number of earthquakes with high magnitude, and RF random forest. without data, the prediction becomes very difficult. The RMSE root mean square error. researches on the prediction use historical data involving an ROC receiver operating characteristics. earthquake's energy, depth, location, and magnitude from SC subtractive clustering. the earthquake catalogs. Based on the magnitude of com- SES seismic electric signal. pleteness value, the area-specific earthquake parameters like b-value parameters are calculated. Machine learning (ML) P0 negative predictive value. based algorithms mainly calculate the seismic indicators like P1 positive predictive value. Sn sensitivity. Gutenberg Richter b-values, time lag, earthquakes energy, Sp specificity. mean magnitude, etc. [3]. Instead deep learning (DL) based SVD singular value decomposition. models can calculate thousands of sophisticated features by SVM/R support vector machine/regressor. themselves [4], [5]. Since ML and DL based models are TEC total electron content. data-driven and major earthquakes happen in a few cases, it is WIA Willmott's index of agreement. challenging to predict them based on historical data. Some methods predict the major earthquakes by separately training them or adding weights to them, but these models need many I. INTRODUCTION improvements [6]. Earthquake is a natural disaster caused by the movement of Another way for successful prediction is to find some tectonic plates of earth due to the release of its substantial precursors of a major earthquake. Precursors are the changes internal energy. A major earthquake with a magnitude greater in elements in nature before the occurrence of an earth- than five can inflict massive death tolls and huge infrastruc- quake. Earthquake scientists suggest that concentration of tural damages costing billions of dollars. However, if the Radon gas, strange cloud formation, earth's electromagnetic occurrences of an earthquake can be predicted, the magnitude field variations, humidity, temperature of the soil, crustal of destruction can be minimized. A complete earthquake change, etc. can be the possible candidate precursors [7]. prediction procedure should have three types of information: Such generalization may be misleading because there were the magnitude, location, and time of occurrence. Since 2005, many cases found where these precursors were present with- there have been 28,400 occurrences of earthquakes with a out the occurrence of an earthquake, and earthquakes took magnitude of more than five around the world [1]. Fig. 1 place even though there was an absence of these precursors. presents the location of the occurrences from January to According to the International Association of Seismology December 2019 [1]. Observing closely, it is possible to see and Physics of the Earth's Interior (IASPEI), precursor-based 2 VOLUME 8, 2020 M. H. A. Banna et al.: Application of AI in Predicting Earthquakes: State-of-the-Art and Future Challenges FIGURE 1. Earthquakes occurred around the world from January 2019 to December 2019 with magnitude greater or equal to five. In twelve months, 1637 earthquakes happened around the world. The data were collected from the United States Geological Surveys and plotted using ArcGIS software. The red square represents the epicenter of occurrence of the earthquake. earthquake research should have some qualities like- it should also investigated [17]. Data mining techniques are
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