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Originated in the early 1990s, SCGM(1, m ) c model has enjoyed widespread application in the fields of urban planning, society economy prediction and modal control in recent years. However, none of these applications have taken account of time lag effects in the modeling process. Aiming at overcoming the defect, the authors introduced time lag items into SCGM(1, m ) c model and developed a SCGM(1, m ) c model with time lag, then discusses in detail some principal problems in the model, such as parameters estimation, model verifying, model prediction, etc. The model was used on a real slope monitoring project and compared with the conventional SCGM(1, m ) c model. The results show an improvement of average models precision from 1.321 to 0.238 and total average of relative prediction errors from 12.41% to 7.98% when the modeling data length ranges from 29 to 48 in the slope monitoring case.
Originated in the early 1990s, SCGM (1, m) c model has enjoyed widespread application in the fields of urban planning, society economy prediction and modal control in recent years. However, none of these applications have taken account of time lag effects in the modeling process. Aiming at overcoming the defect, the authors introduced time lag items into SCGM (1, m) c model and developed a SCGM (1, m) c model with time lag, then discussing in detail some principal problems in the model, such as parameters estimation, model verifying, model prediction, etc. The model was used on a real slope monitoring project and compared with the conventional SCGM (1, m) c model. The results show an improvement of average models precision from 1.321 to 0.238 and total average of relative prediction errors from 12.41% to 7.98% when the modeling data length ranges from 29 to 48 in the slope monitoring case.