Soil Conservation and Watershed Management Research Department, West Azarbaijan Agricultural and Natural Resources Research and Education Center, AREEO, Urmia, Iran.
School of the Built Environment, Oxford Brookes University, Oxford, OX30BP, UK; Kalman Kando Faculty of Electrical Engineering, Obuda University, Budapest, Hungary.
Environ Res. 2019 Dec;179(Pt A):108770. doi: 10.1016/j.envres.2019.108770. Epub 2019 Sep 23.
Earth fissures are the cracks on the surface of the earth mainly formed in the arid and the semi-arid basins. The excessive withdrawal of groundwater, as well as the other underground natural resources, has been introduced as the significant causing of land subsidence and potentially, the earth fissuring. Fissuring is rapidly turning into the nations' major disasters which are responsible for significant economic, social, and environmental damages with devastating consequences. Modeling the earth fissure hazard is particularly important for identifying the vulnerable groundwater areas for the informed water management, and effectively enforce the groundwater recharge policies toward the sustainable conservation plans to preserve existing groundwater resources. Modeling the formation of earth fissures and ultimately prediction of the hazardous areas has been greatly challenged due to the complexity, and the multidisciplinary involved to predict the earth fissures. This paper aims at proposing novel machine learning models for prediction of earth fissuring hazards. The Simulated annealing feature selection (SAFS) method was applied to identify key features, and the generalized linear model (GLM), multivariate adaptive regression splines (MARS), classification and regression tree (CART), random forest (RF), and support vector machine (SVM) have been used for the first time to build the prediction models. Results indicated that all the models had good accuracy (>86%) and precision (>81%) in the prediction of the earth fissure hazard. The GLM model (as a linear model) had the lowest performance, while the RF model was the best model in the modeling process. Sensitivity analysis indicated that the hazardous class in the study area was mainly related to low elevations with characteristics of high groundwater withdrawal, drop in groundwater level, high well density, high road density, low precipitation, and Quaternary sediments distribution.
地裂缝是地表的裂缝，主要形成于干旱和半干旱盆地。地下水的过度抽取以及其他地下自然资源的开采被认为是地面沉降和潜在地裂缝的主要原因。地裂缝正在迅速成为各国的主要灾害，给经济、社会和环境造成重大破坏，带来毁灭性后果。对地裂缝灾害进行建模对于识别脆弱的地下水区域、进行明智的水资源管理以及有效执行地下水补给政策以实现可持续保护规划、保护现有地下水资源至关重要。由于地裂缝的复杂性和多学科性，对地裂缝的形成进行建模，最终进行危险区域的预测，一直面临巨大挑战。本文旨在提出用于地裂缝灾害预测的新型机器学习模型。应用模拟退火特征选择 (SAFS) 方法来识别关键特征，并首次使用广义线性模型 (GLM)、多元自适应回归样条 (MARS)、分类回归树 (CART)、随机森林 (RF) 和支持向量机 (SVM) 来建立预测模型。结果表明，所有模型在预测地裂缝危险方面都具有较高的准确性 (>86%) 和精度 (>81%)。GLM 模型 (作为线性模型) 的性能最低，而 RF 模型在建模过程中是最好的模型。敏感性分析表明，研究区的危险等级主要与具有以下特征的低海拔地区有关：地下水抽取量大、地下水位下降、水井密度高、道路密度高、降水量低和第四纪沉积物分布。
