Choubin Bahram, Shirani Kourosh, Hosseini Farzaneh Sajedi, Taheri Javad, Rahmati Omid
Soil Conservation and Watershed Management Research Department, West Azarbaijan Agricultural and Natural Resources Research and Education Center, AREEO, Urmia, Iran.
Soil Conservation and Watershed Management Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran.
J Environ Manage. 2023 Nov 1;345:118685. doi: 10.1016/j.jenvman.2023.118685. Epub 2023 Jul 28.
Land subsidence is a huge challenge that land and water resource managers are still facing. Radar datasets revolutionize the way and give us the ability to provide information about it, thanks to their low cost. But identifying the most important drivers need for the modeling process. Machine learning methods are especially top of mind amid the prediction studies of natural hazards and hit new heights over the last couple of years. Hence, putting an efficient approach like integrated radar-and-ensemble-based method into practice for land subsidence rate simulation is not available yet which is the main aim of this research. In this study, the number of 52 pairs of radar images were used to identify subsidence from 2014 to 2019. Then, using the simulated annealing (SA) algorithm the key variables affecting land subsidence were identified among the topographical parameters, aquifer information, land use, hydroclimatic variables, and geological and soil factors. Afterward, three individual machine learning models (including Support Vector Machine, SVM; Gaussian Process, GP; Bayesian Additive Regression Tree, BART) along with three ensemble learning approaches were considered for land subsidence rate modeling. The results indicated that the subsidence varies between 0 and 59 cm in this period. Comparing the Radar results with the permanent geodynamic station exhibited a very strong correlation between the ground station and the radar images (R = 0.99, RMSE = 0.008). Parsing the input data by the SA indicated that key drivers are precipitation, elevation, percentage of fine-grained materials in the saturated zone, groundwater withdrawal, distance to road, groundwater decline, and aquifer thickness. The performance comparison indicated that ensemble models perform better than individual models, and among ensemble models, the nonlinear ensemble approach (i.e., BART model combination) provided better performance (RMSE = 0.061, RSR = 0.42, R = 0.83, PBIAS = 2.2). Also, the distribution shape of the probability density function in the non-linear ensemble model is much closer to the observations. Results indicated that the presence of significant fine-grained materials in unconsolidated aquifer systems can clarify the response of the aquifer system to groundwater decline, low recharge, and subsequent land subsidence. Therefore, the interaction between these factors can be very dangerous and intensify subsidence.
地面沉降是土地和水资源管理者仍面临的巨大挑战。雷达数据集因其低成本，彻底改变了我们获取地面沉降信息的方式。但在建模过程中，需要确定最重要的驱动因素。在自然灾害预测研究中，机器学习方法备受关注，且在过去几年达到了新高度。因此，将像基于雷达和集成学习的高效方法应用于地面沉降速率模拟的研究尚未开展，而这正是本研究的主要目标。在本研究中，利用52对雷达图像识别了2014年至2019年的地面沉降情况。然后，使用模拟退火（SA）算法，在地形参数、含水层信息、土地利用、水文气候变量以及地质和土壤因素中，确定了影响地面沉降的关键变量。之后，考虑了三种单独的机器学习模型（包括支持向量机、SVM；高斯过程、GP；贝叶斯加法回归树、BART）以及三种集成学习方法来进行地面沉降速率建模。结果表明，在此期间地面沉降在0至59厘米之间变化。将雷达结果与永久地球动力学站的数据进行比较，结果显示地面站与雷达图像之间具有很强的相关性（R = 0.99，RMSE = 0.008）。通过SA分析输入数据表明，关键驱动因素是降水量、海拔、饱和带细颗粒物质百分比、地下水抽取量、到道路的距离、地下水位下降以及含水层厚度。性能比较表明，集成模型的表现优于单独模型，在集成模型中，非线性集成方法（即BART模型组合）表现更佳（RMSE = 0.061，RSR = 0.42，R = 0.83，PBIAS = 2.2）。此外，非线性集成模型中概率密度函数的分布形状与观测值更为接近。结果表明，在未固结含水层系统中存在大量细颗粒物质，能够解释含水层系统对地下水位下降、补给不足以及随后地面沉降的响应。因此，这些因素之间的相互作用可能非常危险，并加剧地面沉降。
