Geoscience Information Center, Korea Institute of Geoscience & Mineral Resources, (KIGAM), 92, Gwahang-no, Yuseong-gu, Daejeon, 305-350, Korea.
Environ Manage. 2012 Feb;49(2):347-58. doi: 10.1007/s00267-011-9766-5. Epub 2011 Oct 18.
Ground subsidence in abandoned underground coal mine areas can result in loss of life and property. We analyzed ground subsidence susceptibility (GSS) around abandoned coal mines in Jeong-am, Gangwon-do, South Korea, using artificial neural network (ANN) and geographic information system approaches. Spatial data of subsidence area, topography, and geology, as well as various ground-engineering data, were collected and used to create a raster database of relevant factors for a GSS map. Eight major factors causing ground subsidence were extracted from the existing ground subsidence area: slope, depth of coal mine, distance from pit, groundwater depth, rock-mass rating, distance from fault, geology, and land use. Areas of ground subsidence were randomly divided into a training set to analyze GSS using the ANN and a test set to validate the predicted GSS map. Weights of each factor's relative importance were determined by the back-propagation training algorithms and applied to the input factor. The GSS was then calculated using the weights, and GSS maps were created. The process was repeated ten times to check the stability of analysis model using a different training data set. The map was validated using area-under-the-curve analysis with the ground subsidence areas that had not been used to train the model. The validation showed prediction accuracies between 94.84 and 95.98%, representing overall satisfactory agreement. Among the input factors, "distance from fault" had the highest average weight (i.e., 1.5477), indicating that this factor was most important. The generated maps can be used to estimate hazards to people, property, and existing infrastructure, such as the transportation network, and as part of land-use and infrastructure planning.
废弃煤矿区地面沉降可能导致生命和财产损失。我们使用人工神经网络 (ANN) 和地理信息系统方法分析了韩国江原道旌善县废弃煤矿周围的地面沉降易发性 (GSS)。收集了沉降区、地形和地质以及各种地面工程数据的空间数据，并将其用于创建 GSS 图的相关因素栅格数据库。从现有的地面沉降区中提取了 8 个导致地面沉降的主要因素：坡度、煤矿深度、距坑距离、地下水深度、岩体等级、距断层距离、地质和土地利用。地面沉降区被随机划分为训练集，用于使用 ANN 分析 GSS，以及测试集，用于验证预测的 GSS 图。通过反向传播训练算法确定每个因素相对重要性的权重，并将其应用于输入因素。然后使用权重计算 GSS，并创建 GSS 图。该过程重复了十次，使用不同的训练数据集检查分析模型的稳定性。使用未用于训练模型的地面沉降区进行了曲线下面积分析来验证地图。验证结果显示预测准确率在 94.84%至 95.98%之间，总体上表示满意的一致性。在输入因素中，“距断层距离”的平均权重最高（即 1.5477），表明该因素最重要。生成的地图可用于评估对人员、财产和现有基础设施（如交通网络）的危害，并可作为土地利用和基础设施规划的一部分。
