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Prediction of Seawall Settlement Based on a Combined LS-ARIMA Model
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-05-31
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
The analysis and prediction of seawall settlement are important for seawall engineering maintenance and disaster prevention.
Based on the measured seawall settlement time series data, a combined LS-ARIMA forecasting model that fits the trend item by the least-square (LS) method and the season item by the differential self-regression moving average (ARIMA) model was proposed in this study.
The monitoring data of one seawall project in Zhejiang, China, is taken as an example to verify the model efficiency and prediction ability.
The results show that the prediction accuracy of the new combined LS-ARIMA model was high, with the average relative error (ARE) of 0.23%, much better than that of the traditional ARIMA model (ARE = 0.70%) and the GM (1, 1) model (ARE = 33.43%).
This new model has clear physical conception and can effectively improve the prediction accuracy, implying that it can fully tap the dynamic information of monitoring data.
The proposed model in this study provides a new research idea for data analysis and prediction of the seawall settlement.
American Psychological Association (APA)
Qin, Peng& Cheng, Chunmei. 2017. Prediction of Seawall Settlement Based on a Combined LS-ARIMA Model. Mathematical Problems in Engineering،Vol. 2017, no. 2017, pp.1-7.
https://search.emarefa.net/detail/BIM-1191976
Modern Language Association (MLA)
Qin, Peng& Cheng, Chunmei. Prediction of Seawall Settlement Based on a Combined LS-ARIMA Model. Mathematical Problems in Engineering No. 2017 (2017), pp.1-7.
https://search.emarefa.net/detail/BIM-1191976
American Medical Association (AMA)
Qin, Peng& Cheng, Chunmei. Prediction of Seawall Settlement Based on a Combined LS-ARIMA Model. Mathematical Problems in Engineering. 2017. Vol. 2017, no. 2017, pp.1-7.
https://search.emarefa.net/detail/BIM-1191976
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1191976