دورية أكاديمية

Landslide displacement prediction using the GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, China

التفاصيل البيبلوغرافية
العنوان: Landslide displacement prediction using the GA-LSSVM model and time series analysis: a case study of Three Gorges Reservoir, China
المؤلفون: Wen, Tao, Tang, Huiming, Wang, Yankun, Lin, Chengyuan, Xiong, Chengren
بيانات النشر: Copernicus Publications
سنة النشر: 2017
المجموعة: Niedersächsisches Online-Archiv NOA (Gottfried Wilhelm Leibniz Bibliothek Hannover)
مصطلحات موضوعية: article, Verlagsveröffentlichung
الوصف: Predicting landslide displacement is challenging, but accurate predictions can prevent casualties and economic losses. Many factors can affect the deformation of a landslide, including the geological conditions, rainfall and reservoir water level. Time series analysis was used to decompose the cumulative displacement of landslide into a trend component and a periodic component. Then the least-squares support vector machine (LSSVM) model and genetic algorithm (GA) were used to predict landslide displacement, and we selected a representative landslide with episodic movement deformation as a case study. The trend component displacement, which is associated with the geological conditions, was predicted using a polynomial function, and the periodic component displacement which is associated with external environmental factors, was predicted using the GA-LSSVM model. Furthermore, based on a comparison of the results of the GA-LSSVM model and those of other models, the GA-LSSVM model was superior to other models in predicting landslide displacement, with the smallest root mean square error (RMSE) of 62.4146 mm, mean absolute error (MAE) of 53.0048 mm and mean absolute percentage error (MAPE) of 1.492 % at monitoring station ZG85, while these three values are 87.7215 mm, 74.0601 mm and 1.703 % at ZG86 and 49.0485 mm, 48.5392 mm and 3.131 % at ZG87. The results of the case study suggest that the model can provide good consistency between measured displacement and predicted displacement, and periodic displacement exhibited good agreement with trends in the major influencing factors.
نوع الوثيقة: article in journal/newspaper
وصف الملف: electronic
اللغة: English
العلاقة: Natural Hazards and Earth System Sciences -- http://www.bibliothek.uni-regensburg.de/ezeit/?2064587Test -- http://www.nat-hazards-earth-syst-sci.netTest/ -- 1684-9981; https://doi.org/10.5194/nhess-17-2181-2017Test; https://noa.gwlb.de/receive/cop_mods_00007825Test; https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00007782/nhess-17-2181-2017.pdfTest; https://nhess.copernicus.org/articles/17/2181/2017/nhess-17-2181-2017.pdfTest
DOI: 10.5194/nhess-17-2181-2017
الإتاحة: https://doi.org/10.5194/nhess-17-2181-2017Test
https://noa.gwlb.de/receive/cop_mods_00007825Test
https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00007782/nhess-17-2181-2017.pdfTest
https://nhess.copernicus.org/articles/17/2181/2017/nhess-17-2181-2017.pdfTest
حقوق: uneingeschränkt ; info:eu-repo/semantics/openAccess
رقم الانضمام: edsbas.FE09C7A5
قاعدة البيانات: BASE