Abstract
Preventive protection of cultural relics is to make use of all the science and technology beneficial to the research and protection of archaeological heritage to predict the disease of cultural relics. The existing preventive cultural relics protection system has made some achievements in environmental monitoring, but the analysis and utilization of large data of cultural relics are still insufficient. In this paper, under the idea of multisource information fusion, a least squares support vector machine regression method based on multivariate time series wavelet correlation analysis is proposed to achieve accurate crack prediction of stone cultural relics. Firstly, the correlation of multivariate time series of stone cultural relics are quantitatively analyzed and the validity of characteristic variables of the crack is discriminated by wavelet correlation analysis; then, a least squares support vector machine prediction model is constructed based on the correlation obtained from the analysis; finally, the good performance of the method is verified by using the environmental monitoring data of the rock mass fracture in the North Qianfo Cliff of Dafo Temple in Binzhou City of Shaanxi Province. The experimental results show that the proposed method is more effective than the traditional backpropagation neural network, support vector machine, and relevance vector machine regression methods. This method is universal and easy to implement for multisource data prediction of nonmovable cultural relics diseases. It provides a scientific theoretical reference for the preventive protection of cultural relics.
Highlights
Immovable cultural relics refer to ancient cultural sites, ancient tombs, ancient buildings, cave temples, stone carvings, murals, important modern historical sites, and representative buildings. e protection of cultural heritage is the foundation of maintaining the world cultural diversity and inheriting human civilization and the responsibility of mankind [1, 2].Immovable cultural relics diseases are mainly influenced by natural and man-made environmental factors [3]. ese datasets of environmental factors are monitored by sensors and usually stored in the form of multiple time series; these environmental factors include wind speed, temperature, humidity, and light, which are usually characterized by high dimensions, complex correlations, and nonlinearity
In order to verify the effectiveness of the proposed wavelet correlation analysis least squares support vector machine (LSSVM)-based method for crack prediction (WCA-LSSVM-CP), it is compared with the existing method based on backpropagation neural network (BPNN) (WCA-BPNN-CP), standard support vector machine (SVM) (WCA-SVM-CP), and relevance vector machine (RVM) (WCA-RVMCP). e case data set adopts the environmental monitoring data of rock mass fractures provided by Shaanxi PI Culture and Education Technology Co., LTD., including the temperature and humidity, rainfall, SO2, O3, NO2, organic volatile, light intensity, wind speed, wind direction, and crack disease data
Crack Prediction Results and Discussion. e environment variable X′′(t) after dimension reduction was used as the input to LSSVM, BPNN, and the standard SVM and RVM prediction model. e first 405 sets of data were selected as the training samples, and the last 55 groups were the test samples. e prediction results are obtained by simulation, as shown in Figures 5 and 6
Summary
Immovable cultural relics refer to ancient cultural sites, ancient tombs, ancient buildings, cave temples, stone carvings, murals, important modern historical sites, and representative buildings. e protection of cultural heritage is the foundation of maintaining the world cultural diversity and inheriting human civilization and the responsibility of mankind [1, 2]. Is paper proposes a least squares support vector machine (LSSVM) regression method based on WCA of multiple time series, which predicts accurately the diseases of immovable cultural relics. (i) e main factors affecting stone cultural relics crack are obtained quantitatively by the WCA method, and the dimension reduction of data is realized without affecting the prediction accuracy of the model. Complexity, and redundancy of environmental data, as well as the different units and value ranges of each variable attribute, the preprocessing of environmental data of stone cultural relics can be divided into two steps: (I) data normalization [28] and (II) dimension reduction of independent variables. E wavelet correlation analysis method is adopted to calculate the correlation coefficient matrix between the normalized multivariate time series independent variable X′(t) and the dependent variable Y′(t) using formula (8), denoted as Rxy(n × m), as follows [28]:. E purpose of dimensionality reduction of the independent variable is to reduce redundant variables, which improves the accuracy of the prediction model and provides scientific theoretical guidance for reducing equipment investment in data collection
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