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Abstract
An earlier study carried out in 2010 at the archaeological site of L’Almoina (Valencia, Spain) found marked daily fluctuations of temperature, especially in summer. Such pronounced gradient is due to the design of the museum, which includes a skylight as a ceiling, covering part of the remains in the museum. In this study, it was found that the thermal conditions are not homogeneous and vary at different points of the museum and along the year. According to the European Standard EN10829, it is necessary to define a plan for long-term monitoring, elaboration and study of the microclimatic data, in order to preserve the artifacts. With the aforementioned goal of extending the study and offering a tool to monitor the microclimate, a new statistical methodology is proposed. For this propose, during one year (October 2019–October 2020), a set of 27 data-loggers was installed, aimed at recording the temperature inside the museum. By applying principal component analysis and k-means, three different microclimates were established. In order to characterize the differences among the three zones, two statistical techniques were put forward. Firstly, Sparse Partial Least Squares Discriminant Analysis (sPLS-DA) was applied to a set of 671 variables extracted from the time series. The second approach consisted of using a random forest algorithm, based on the same functions and variables employed by the first methodology. Both approaches allowed the identification of the main variables that best explain the differences between zones. According to the results, it is possible to establish a representative subset of sensors recommended for the long-term monitoring of temperatures at the museum. The statistical approach proposed here is very effective for discriminant time series analysis and for explaining the differences in microclimate when a net of sensors is installed in historical buildings or museums.
Highlights
The environmental conditions of historical buildings, exhibition facilities and storage areas in museums have been shown to be the most crucial factor in the preservation of collections and artifacts
Regarding random forest (RF), the OOB classification error was used as an estimate of the test error and the parameters Mean Decrease in Gini (MDG) and Mean Decrease Accuracy (MDA) were computed as indicators of the variable importance
The results suggest that the number of variables for the first component of each method were the following: 15 (M1 and M2), 5 (M3 and M4) and 15 for Ms The information displayed in Figures 9 and 10 was used to apply the final model
Summary
The environmental conditions of historical buildings, exhibition facilities and storage areas in museums have been shown to be the most crucial factor in the preservation of collections and artifacts. Many studies have been conducted in recent years which monitor the climatic parameters for the long-term preservation of cultural heritage. Huijbregts et al [15] proposed a method for evaluating the damage risk of long-term climate changes on artifacts in museums or historic buildings. This method was applied for two historic museums in the Netherlands and Belgium. Zítek and Vyhlídal [16] proposed a novel air humidity control technique for preventing the moisture sensitive materials from varying the equilibrium of their moisture content, maintaining desirable environmental conditions for the preventive conservation of cultural heritage. Lourenço et al [18] studied air T and RH, among other parameters, in the historical city center of Bragaça (Portugal)
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