Research on evaluation factors and algorithms of dam monitoring data quality
Dam monitoring data is the main basis for judging the safety of dam operation.In order to identify the data quality and select the data with high reliability,a dam monitoring data quality evaluation framework is constructed.According to the correlation between measuring points and the features of monitoring items and instruments,Kshape algorithm is used to find out the measuring points with strong correlation,and then the dam monitoring data is evaluated by means of the evaluation factors such as relative offset rate,relative smoothness rate,periodic fluctuation degree and accuracy correction rate.In combination with the LSTM(long short-term memory network)optimized by hybrid bat algorithm,the dam monitoring data is classified,and the algorithm flow of dam monitoring data quality evaluation is constructed.The test is conducted by taking a dam monitoring data in Xinjiang as the research object.The results show that the accuracy of the proposed dam monitoring data quality evaluation algorithm is 94.33%,which can provide an effective solution for evaluating the quality of dam monitoring data.
dam monitoring dataevaluation factordata quality evaluationlong short-term memory networkmeasuring point clusteringcorrelation analysis
万方数据
维普
