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.

中文翻译：

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Haskell的确定性树突状细胞算法在地震震级预测中的应用

地震震级预测是一项极为艰巨的任务，已通过使用许多不同的策略来解决，目前为止没有进一步的转换。这项工作评估了基于Haskell的确定性树突状细胞算法（hDCA）在预测四川及周边地区地震烈度时的准确性。首先，从文献中检索出八个地震活动度指标，并将其用作算法的输入，并根据著名的地球物理理论古登堡-里希特逆幂定律从四川及周边地区的地震目录中计算出这些指标地震震级分布以及最近相关研究得出的结论。然后，将hDCA用来预测下个月大于4.5级的地震。在这项工作中 将该方法与著名的机器学习算法进行了比较，例如树突状细胞算法（DCA），支持向量机（SVM），K最近邻（KNN），反向传播神经网络（BPNN），递归神经网络（RNN），概率神经网络（PNN）和神经动态分类（NDC）。总体而言，我们的方法在评估的所有合格参数方面均产生了令人鼓舞的结果。