| Abstrakt: |
An accurate prediction of the future condition of museum objects is crucial for developing appropriate proactive maintenance and preservation strategies. Despite this, there are very few such damage models that can be used in practice. The main reasons, for this lack of deterioration models, include complexity of deterioration problem and lack of understanding of the degradation mechanisms affecting various materials and objects, and lack of reliable quantitative approaches. In the article, we discuss the machine learning model, which predicts the future condition of museum objects. For this purpose, the model uses the data of MuIS (Estonian Museum Information System). To predict deterioration, we experimented primarily with various tree-based machine learning algorithms, such as the decision tree, the random forest, and XGBoost. The best results were obtained using the decision forest algorithm, which was able to identify 92% of deteriorating museum objects with 50% accuracy. The machine learning model provides a way to model ageing processes of museum objects over the course of time and thus better plan the preservation work of museums. [ABSTRACT FROM AUTHOR] |
