基于极限学习机的汉阳陵外藏坑遗址温度预测研究

Temperature forecasting for an enclosed earthen site museum based on extreme learning machine analysis

作为我国首座全封闭式遗址博物馆,汉阳陵外藏坑遗址博物馆已积累超过700万条各类监测数据。然而,受限于海量数据挖掘能力,目前研究中存在监测数据利用率不足和预测模型准确率较低的问题,针对这一问题,引入大数据机器学习领域的极限学习机方法对遗址温度进行系统分析和建模预测。研究结果表明,该方法具有良好的大数据发掘和处理能力,能够有效分析温度的变化规律及特征,并对未来温度变化趋势和细节变化特征实现较为准确的预测。本方法的引入可为遗址预防性保护和管理提供参考。

Known as the first enclosed earthen site museum in China, the Outer Burial Pits of the Han Dynasty Yang Mausoleum have accumulated more than 7 million pieces of environmental monitoring data of various types. However, due to limitations of big-data mining, utilization of the monitoring data is insufficient and the accuracy of forecasting models is low. In order to solve the problems, a big-data machine learning method, known as the extreme learning machine(ELM), was introduced for temperature analysis and forecasting. The results show that ELM can analyze the annual temperature monitoring data effectively and predicts the instantaneous temperature trends in the future. The introduction of this method represents a useful reference for preventive conservation and management of historical sites.