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The deterministic dendritic cell algorithm with Haskell in earthquake magnitude prediction

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Abstract

The earthquake magnitude prediction is a task of utmost difficulty that has been addressed by using many different strategies, with no further transformation thus far. This work evaluates the Haskell based deterministic dendritic cell algorithm (hDCA)’s accuracy when used to predict earthquake magnitude in Sichuan and surroundings. First, eight seismicity indicators have been retrieved from the literature and used as input for the algorithms, and they are calculated from the earthquake catalog of the Sichuan and surroundings by well-known geophysical theory, named Gutenberg-Richter inverse power-law, and characteristic earthquake magnitude distribution and also conclusions drawn by recent related studies. Then, the hDCA is used to predict earthquakes with magnitude larger than 4.5 in the next month. In this work, the proposed method has been compared to the well-known machine learning algorithms, such as Dendritic Cell Algorithm (DCA), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Back Propagation Neural Network (BPNN), Recurrent Neural Network (RNN), Probabilistic Neural Network (PNN) and Neural Dynamic Classification (NDC). Overall our method yields the promising results in terms of all qualify parameters evaluated.

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Acknowledgments

The authors want to thank NSFC- http://www.nsfc.gov.cn/ for the support through Grants Number 61877045, and Military Commission Innovation Special Zone for the support through Grants Number 17-H863-01-ZT-002-016-02, and Fundamental Research Project of Shenzhen Science and Technology Program for the support through Grants Number JCYJ20160428153956266.

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Correspondence to Wen Zhou.

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Communicated by: H. Babaie

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Zhou, W., Dong, H. & Liang, Y. The deterministic dendritic cell algorithm with Haskell in earthquake magnitude prediction. Earth Sci Inform 13, 447–457 (2020). https://doi.org/10.1007/s12145-020-00442-z

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