The prediction of the earthquake has been a testing investigation field, where a prediction of the impending incidence of destructive calamity is made. In this research, eight seismic features are processed by utilizing seismological notions, such as seismic quiescence, the eminent geophysical specifics of Gutenberg–Richter’s inverse law, and dissemination of typical earthquake extents for earthquake prediction. A classification system based on support vector regressor (SVR) along with hybrid neural network (HNN) is formed to attain the predictions of earthquakes for the Hindukush region. The challenge is expressed as a binary classification undertaking, and for earthquakes of magnitude equal to or more than 5.5, the predictions are generated for 1 month. HNN is a step-by-step amalgamation of three diverse neural networks, and enhanced particle swarm optimization (EPSO) is used to extend weight optimization at an individual layer, thus enhancing the performance of HNN. In amalgamation with the SVR-HNN prediction system, the freshly processed seismic aspects are applied to the Hindukush region. For analyzing the results, another considered performance measure is accuracy. Comparative to earlier prediction investigations, the achieved numerical outcomes demonstrate enhanced prediction implementation for the considered region.

地震的预测一直是一个试验研究领域，在那里对即将发生的破坏性灾难进行预测。在这项研究中，利用地震学概念处理了八个地震特征，例如地震静息，古腾堡-里希特逆定律的显着地球物理特征以及用于地震预测的典型地震范围的传播。建立了基于支持向量回归（SVR）和混合神经网络（HNN）的分类系统，以实现对Hindukush地区地震的预测。挑战用二进制分类法来表示，对于5.5级或以上的地震，将产生1个月的预测。HNN是三个不同神经网络的逐步合并，增强的粒子群优化（EPSO）用于扩展单个层的权重优化，从而增强HNN的性能。在与SVR-HNN预测系统合并后，将新处理的地震方面应用于Hindukush地区。为了分析结果，另一个考虑的性能指标是准确性。与早期的预测研究相比，获得的数值结果证明了所考虑区域的预测实施得到了增强。