基于数据挖掘的中国矿石储量预测模型的研究与应用

Research and Application of China’s Mineral Reserves Prediction Model Based on Data Mining

摘要: 随着中国矿产资源需求的持续增长，矿产储量的科学预测对于资源管理和可持续发展至关重要。采用多种预测模型(如线性回归、多项式回归、Prophet、ARIMA及指数平滑法)对中国主要矿产的储量进行了预测分析。通过比较各模型在不同矿产资源上的表现，发现大多数模型能够较好地反映矿产储量的变化趋势。对于储量变化平稳的矿产，预测效果较为准确，而对于波动较大的矿产，误差有所增加。研究结果为矿业公司和政府提供了科学的决策支持，促进了资源的合理开发和环境保护。随着技术的进步，未来的储量预测模型有望进一步提升精度和适应性。

Abstract: With the continuous growth of demand for mineral resources in China, the scientific prediction of mineral reserves is crucial for resource management and sustainable development. This study adopts a variety of prediction models (such as linear regression, polynomial regression, Prophet, ARIMA, and exponential smoothing method) to conduct predictive analysis on the reserves of China’s major minerals. By comparing the performance of each model on different mineral resources, it is found that most models can well reflect the changing trend of mineral reserves. For minerals with stable reserve changes, the prediction effect is relatively accurate; however, for minerals with large fluctuations, the prediction error increases. The research results provide scientific decision support for mining companies and the government, and promote the rational development of resources and environmental protection. With the advancement of technology, future reserve prediction models are expected to further improve their accuracy and adaptability.