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.

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leastsquare ls method trend item seawall project dynamic information of monitoring season differential selfregression moving average arima model measured seawall settlement time series data analysis and prediction of the seawall settlement seawall engineering maintenance and disaster prevention average relative error are of accuracy gm 1 1 model combined lsarima forecasting model monitoring data

Peng Qin,Chunmei Cheng,.Prediction of Seawall Settlement Based on a Combined LS-ARIMA Model. 2017 (),.

Peng Qin,Chunmei Cheng,"Prediction of Seawall Settlement Based on a Combined LS-ARIMA Model" 2017

Peng Qin,Chunmei Cheng,"Prediction of Seawall Settlement Based on a Combined LS-ARIMA Model"