山崩
计算机科学
数据挖掘
边坡稳定性
利用
Boosting(机器学习)
理论(学习稳定性)
决策树
地质学
机器学习
岩土工程
计算机安全
作者
Lianbo Ma,Jingwei Wang,Jian Cheng,Xingwei Wang,Wancheng Zhu
出处
期刊:IEEE transactions on artificial intelligence
[Institute of Electrical and Electronics Engineers]
日期:2022-02-01
卷期号:3 (1): 78-87
被引量:2
标识
DOI:10.1109/tai.2021.3114652
摘要
The mine landslide risk prediction is a fundamental task for the safety management of the digital mining system, which is dependent on the analysis of the open pit mine exploitation slope stability. Such stability analysis involves a lot of local natural and human factors. Training a learning model using these factors, with the aim of discovering their relationship with the slope stability, is an intuitive way for the mine landslide risk prediction. The key issue is how to explore the complex nonlinear relations among these factors by using a small amount of high-dimensional historical slope data. Traditional factor-led methods for the issue only focus on the impacts of landslide factors, but ignore the correlations between historical slope data, which in fact contain more useful information. In this article, we propose a new mine landslide risk prediction model using knowledge graph. The gradient boosting decision tree is applied to further exploit the crossed features within the historical data, and then a landslide semantic network is constructed using knowledge graph with the consideration of correlation information between historical slope data. In this way, the model is suitable to deal with the small set of high-dimensional data, and it makes a joint use of the features within the landslide factor values and correlations between historical slope data. We conduct a set of experiments on real historical slope data from the real-world open pit mining scene. The experiment results validate the effectiveness and efficiency of the proposed model in predicting landslide risks with a small set of high-dimensional historical data.
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