Land subsidence (LS) is significant problem that can lead to casualties, destruction of infrastructure, and socio-economic and environmental problems. In this study, we examine the Damghan Plain of Iran where LS poses a major obstacle to growth and management of the region. Dagging and random subspace (RSS) as meta- or ensemble-classifiers of a radial basis function neural network (RBFnn) were combined into two novel-ensemble intelligence approaches (Dagging-RBFnn and RSS-RBFnn) to predict and map the susceptibility of land units to subsidence. The goodness-of-fit (of training data) and prediction accuracy (of testing data) for the ensemble models were contrasted with the RBFnn, which is used as the benchmark for improvement. Details of 120 LS locations were examined and the data for twelve LS conditioning factors (LSCFs) were compiled. The LS points were randomly divided into four groups or folds, each comprised of 25 percent of the cases. The novel ensemble models were constructed using 75 percent (3 folds) and tested with the remaining 25 percent (onefold) in a four-fold cross-validation (CV) mechanism. Information-gain ratio and multicollinearity tests were used to select the LSCFs that would be used to estimate LS probabilities. The importance of each factor was calculated using a random forest (RF) model. The most important LSCFs were groundwater drawdown, land uses and land covers, elevation, and lithology. Twelve land subsidence susceptibility maps were generated using the k-fold CV approaches as each of the three models (RBFnn, Dagging-RBFnn and RSS-RBFnn) was applied to each of the four folds. The LS susceptibility models reveal a strong probability for LS on 15% to 24% of the plain. All of the maps generated achieved adequate levels of prediction accuracies and goodness-of-fits. The Dagging-RBFnn ensemble yielded the most robust maps, however. The ensemble of Dagging-RBFnn enhances the accuracy of modeling but the opposite condition was found for the RSS-RBFnn ensemble. It is evident that ensembles with meta classifiers might not always increase the accuracy of the base classifier. Overall, the southern part of the plain shows the highest LS risk. The results of this study suggests that groundwater withdrawal levels should be tracked and possibly restricted in regions with higher (extreme or moderate) probabilities of LS. This demonstrates that new approaches can support land use planning and decision making to minimize LS and improve sustainability.

地面沉降 (LS) 是一个重大问题，可导致人员伤亡、基础设施破坏以及社会经济和环境问题。在这项研究中，我们研究了伊朗的 Damghan 平原，在那里 LS 对该地区的增长和管理构成了主要障碍。作为径向基函数神经网络 (RBFnn) 的元分类器或集成分类器的 Dagging 和随机子空间 (RSS) 被组合成两种新颖的集成智能方法（Dagging-RBFnn 和 RSS-RBFnn），以预测和映射土地的敏感性单位下沉。集成模型的（训练数据的）拟合优度和（测试数据的）预测精度与 RBFnn 进行了对比，后者用作改进的基准。检查了 120 个 LS 位置的详细信息，并汇编了 12 个 LS 调节因子 (LSCF) 的数据。LS 点被随机分为四组或折叠，每组包括 25% 的病例。新的集成模型是使用 75%（3 倍）构建的，并在四倍交叉验证 (CV) 机制中使用剩余的 25%（单倍）进行测试。信息增益比和多重共线性测试用于选择用于估计 LS 概率的 LSCF。使用随机森林（RF）模型计算每个因素的重要性。最重要的 LSCF 是地下水下降、土地利用和土地覆盖、海拔和岩性。当将三个模型（RBFnn，Dagging-RBFnn和RSS-RBFnn）中的每一个应用于四个褶皱时，使用k折CV方法生成了十二张地面沉降敏感性图。LS 敏感性模型显示 15% 到 24% 的平原上发生 LS 的可能性很大。生成的所有地图都达到了足够的预测精度和拟合优度。然而，Dagging-RBFnn 集成产生了最强大的地图。Dagging-RBFnn 的集成提高了建模的准确性，但 RSS-RBFnn 集成发现了相反的条件。很明显，带有元分类器的集成可能并不总是提高基分类器的准确度。总体而言，平原南部的 LS 风险最高。这项研究的结果表明，在 LS 概率较高（极端或中等）的地区，应跟踪并可能限制地下水抽取水平。