High-Resolution Mining-Induced Geo-Hazard Mapping Using Random Forest: A Case Study of Liaojiaping Orefield, Central China

Mining-induced geo-hazard mapping (MGM) is a critical step for reducing and avoiding tremendous losses of human life, mine production, and property that are caused by ore mining. Due to the restriction of the survey techniques and data sources, high-resolution MGM remains a big challenge. To overcome this problem, in this research, such an MGM was conducted using detailed geological exploration and topographic survey data as well as Gaofen-1 satellite imagery as multi-source geoscience datasets and machine learning technique taking Liaojiaping Orefield, Central China as an example. First, using Gaofen-1 panchromatic and multispectral (PMS) sensor data and Random Forest (RF) non-parametric ensemble classifier, a seven-class land cover map was generated for the study area with an overall accuracy (OA) and Kappa coefficient (KC) of 99.69% and 98.37%, respectively. Next, several environmental drivers including land cover, topography (aspect and slope), lithology, distance from fault, elevation difference between surface and underground excavation, and the difference of spectral information from PMS multispectral data of different years were integrated as predictors to construct an RF-based MGM model. The constructed model showed an excellent prediction performance, with an OA of 98.53%, KC of 97.06%, and AUC of 0.998, and the 85.60% of the observed geo-disaster that have occurred in the predicted high susceptibility class (encompassing 2.82% of the study area). The results suggested that the changes in environmental factors in the high susceptibility areas can be used as indicators for monitoring and early-warning of the geo-disaster occurrence.

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