环境数据失真条件下土石坝变形预测方法研究OA

During the daily operation and management of an earth-rock dam,data monitoring often suffers from difficulties caused by environmental data distortion and even data sequence gaps or interruptions.Owing to this challenge,traditional prediction methods have struggled to conduct a scientific and reliable analysis of the dam's deformation behaviors and health conditions.This paper presents a novel prediction method for earth-rock dam deformation,based on a single-time-series optimization model and the multi-scale combination theory.We consider the strong time-dependence of these deformation behaviors,and construct a Transformer single-time-series training model that features an excellent capability of global dependency learning to capture the autocorrelation of target variables accurately.And,variational mode decomposition is adopted to implement frequency-domain preprocessing of the training samples to reduce cross-interference from multi-frequency components and noise within the data.Further,we use the Kepler optimization algorithm to optimize the decomposition parameters and the historical information volume input for sub-sequence training,and thereby achieve a deformation prediction model for earth-rock dams under the condition of environmental data distortion.Case studies demonstrate this new method presents satisfactory prediction performance and strong generalization capability of handling non-stationary and low-quality monitoring data,showing a promising potential for practical deformation analysis of earth-rock dams under complicated monitoring conditions.