Earthquake prediction in California using regression algorithms and cloud-based big data infrastructure

Asencio-Cortés, Gualberto; Morales‐Esteban, Antonio; Shang, Xueyi; Martínez‐Álvarez, Francisco

doi:10.1016/j.cageo.2017.10.011

Cited by 73 publications

(28 citation statements)

References 34 publications

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“…Reference [58] applied Support Vector regression and Hybrid neural Network for earthquake prediction in Hindukush, Chile and Southern California regions with prediction accuracy rate of 82.7%, 84.9%, 90.6% respectively. Reference [59] analyzed earthquake magnitude prediction on the basis of regression algorithms and cloud based big data infrastructure. Reference [60] used grid-search method to construct support vector machine (SVM) based model for earthquake prediction.…”

Section: Discussionmentioning

confidence: 99%

“…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.…”

Section: Basic Analysismentioning

confidence: 99%

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Earthquake Prediction Using Expert Systems: A Systematic Mapping Study

2020

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Earthquake is one of the most hazardous natural calamity. Many algorithms have been proposed for earthquake prediction using expert systems (ES). We aim to identify and compare methods, models, frameworks, and tools used to forecast earthquakes using different parameters. We have conducted a systematic mapping study based upon 70 systematically selected high quality peer reviewed research articles involving ES for earthquake prediction, published between January 2010 and January 2020.To the best of our knowledge, there is no recent study that provides a comprehensive survey of this research area. The analysis shows that most of the proposed models have attempted long term predictions about time, intensity, and location of future earthquakes. The article discusses different variants of rule-based, fuzzy, and machine learning based expert systems for earthquake prediction. Moreover, the discussion covers regional and global seismic data sets used, tools employed, to predict earth quake for different geographical regions. Bibliometric and meta-information based analysis has been performed by classifying the articles according to research type, empirical type, approach, target area, and system specific parameters. Lastly, it also presents a taxonomy of earthquake prediction approaches, and research evolution during the last decade.

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Section: Discussionmentioning

confidence: 99%

Section: Basic Analysismentioning

confidence: 99%

Earthquake Prediction Using Expert Systems: A Systematic Mapping Study

2020

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“…Asencio-Cortés et al [118] proposed a cloud-based big data analysis of earthquake magnitude prediction within 7 days of occurrence, using 5 regression algorithms. They have considered DL, generalized linear models (GLM), RF, gradient boosting machines, and stacking ensemble of these algorithms for this purpose.…”

Section: ) Earthquake and Aftershock Prediction Studiesmentioning

confidence: 99%

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

et al. 2020

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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 paper 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.

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“…In addition to this micro-seismic event prediction, Derakhshani and Foruzan [40] developed DL neural networks to estimate seismic ground motion parameters. Another study on earthquake magnitude prediction has been carried out based on regression algorithms and cloud-based big data infrastructure [41]. In the mentioned study, it was shown that using big data analytics for predicting the magnitude of earthquakes opens a very promising research area and the methodology may enable simultaneous processing of massive data with a large number of variables.…”

Section: Previous Studies On the Use Of Cnn In Geosciencesmentioning

confidence: 99%

A Convolutional Neural Network Architecture for Auto-Detection of Landslide Photographs to Assess Citizen Science and Volunteered Geographic Information Data Quality

Can

Kocaman

Gökçeoğlu

2019

IJGI

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Several scientific processes benefit from Citizen Science (CitSci) and VGI (Volunteered Geographical Information) with the help of mobile and geospatial technologies. Studies on landslides can also take advantage of these approaches to a great extent. However, the quality of the collected data by both approaches is often questionable, and automated procedures to check the quality are needed for this purpose. In the present study, a convolutional neural network (CNN) architecture is proposed to validate landslide photos collected by citizens or nonexperts and integrated into a mobile- and web-based GIS environment designed specifically for a landslide CitSci project. The VGG16 has been used as the base model since it allows finetuning, and high performance could be achieved by selecting the best hyper-parameters. Although the training dataset was small, the proposed CNN architecture was found to be effective as it could identify the landslide photos with 94% precision. The accuracy of the results is sufficient for purpose and could even be improved further using a larger amount of training data, which is expected to be obtained with the help of volunteers.

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Earthquake prediction in California using regression algorithms and cloud-based big data infrastructure

Cited by 73 publications

References 34 publications

Earthquake Prediction Using Expert Systems: A Systematic Mapping Study

Earthquake Prediction Using Expert Systems: A Systematic Mapping Study

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

A Convolutional Neural Network Architecture for Auto-Detection of Landslide Photographs to Assess Citizen Science and Volunteered Geographic Information Data Quality

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