Research on prediction algorithm of ship equipment heath condition

Chen, Dejun; Tang, Tiantong; Yao, Yongan

doi:10.1016/j.oceaneng.2022.110750

Cited by 5 publications

(1 citation statement)

References 26 publications

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“…The seasonal autoregressive moving average model can be used not only to predict natural phenomena such as river flow and rainfall [17][18][19][20] but also to predict social and economic phenomena such as transportation passenger flow and trade volume [21][22][23][24][25]. In the field of earthquake prediction, some scholars have used ARIMA to identify earthquake precursor anomalies [26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

Analysis and Prediction of the SARIMA Model for a Time Interval of Earthquakes in the Longmenshan Fault Zone

Yuan¹,

Huang²,

Qiuyin³

et al. 2023

Natural Hazards - New Insights

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Based on the catalog data of earthquakes with Ms ≥ 2.5 in the Longmenshan fault zone from January 2012 to September 2021, we establish an earthquake time interval series grouped by earthquake magnitude and then use the SARIMA model to predict the series in different periods. By analyzing the fitting effect of the models, the optimal model parameters of different magnitude sequences and the corresponding period values are obtained. Among them, the adjusted R2 values of each model with Ms ≥ 2.5 and Ms ≥ 3.0 sequences are more than 0.86, up to 0.911; the short-time prediction effects are good, and the values of predicted RMSE are 10.686 and 8.800. The prediction results of the models show that the overall trend of the subsequent earthquake time interval in the Longmenshan fault zone is stable, and the prediction results of the Ms ≥ 3.0 sequence have a weak fluctuating growth trend; that is, the number of earthquakes with the Ms ≥ 3.0 in this area will decrease slightly, and the seismicity will decrease in a period of time. The analysis results and method can provide a scientific basis for earthquake risk management and a feasible way to predict earthquake occurrence times.

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