Dam deformation prediction method, system and equipment and storage medium
The invention provides a dam deformation prediction method, system and device and a storage medium, and relates to the field of dam safety monitoring. The method comprises the following steps: obtaining dam monitoring data, wherein the dam monitoring data comprises a target deformation amount and a...
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creator | TAN YONG CHI HONGYOU TIAN YALING HU LEI HONG LIYANG QIN PENG LI BO YANG SHENGMEI HU CHAO LIU KANG WEI XUBEI ZHANG QILING CHEN LI GAO XIAOFENG GAN XIAOQING |
description | The invention provides a dam deformation prediction method, system and device and a storage medium, and relates to the field of dam safety monitoring. The method comprises the following steps: obtaining dam monitoring data, wherein the dam monitoring data comprises a target deformation amount and a plurality of input characteristics; performing data mining on the dam monitoring data based on a Lasso regression algorithm to obtain a feature weight, corresponding to the target deformation, of each input feature; sorting and/or screening the plurality of input features based on the feature weights, corresponding to the target deformation, of the input features to obtain optimized input features; and the optimized input features are sent to a preset encoder part of the LSTM network, and a decoder part of the LSTM network introduces an attention mechanism to perform time sequence prediction on the prediction target deformation based on the output of the encoder and the result of the attention mechanism. According |
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The method comprises the following steps: obtaining dam monitoring data, wherein the dam monitoring data comprises a target deformation amount and a plurality of input characteristics; performing data mining on the dam monitoring data based on a Lasso regression algorithm to obtain a feature weight, corresponding to the target deformation, of each input feature; sorting and/or screening the plurality of input features based on the feature weights, corresponding to the target deformation, of the input features to obtain optimized input features; and the optimized input features are sent to a preset encoder part of the LSTM network, and a decoder part of the LSTM network introduces an attention mechanism to perform time sequence prediction on the prediction target deformation based on the output of the encoder and the result of the attention mechanism. 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The method comprises the following steps: obtaining dam monitoring data, wherein the dam monitoring data comprises a target deformation amount and a plurality of input characteristics; performing data mining on the dam monitoring data based on a Lasso regression algorithm to obtain a feature weight, corresponding to the target deformation, of each input feature; sorting and/or screening the plurality of input features based on the feature weights, corresponding to the target deformation, of the input features to obtain optimized input features; and the optimized input features are sent to a preset encoder part of the LSTM network, and a decoder part of the LSTM network introduces an attention mechanism to perform time sequence prediction on the prediction target deformation based on the output of the encoder and the result of the attention mechanism. According</abstract><oa>free_for_read</oa></addata></record> |
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subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING ELECTRIC DIGITAL DATA PROCESSING MEASURING MEASURING ANGLES MEASURING AREAS MEASURING IRREGULARITIES OF SURFACES OR CONTOURS MEASURING LENGTH, THICKNESS OR SIMILAR LINEARDIMENSIONS PHYSICS TESTING |
title | Dam deformation prediction method, system and equipment and storage medium |
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