Abstract：
This contribution presents a novel methodology based on the feature selection, ensemble deep learning (EDL) models, and active learning (AL) approach for prediction of land subsidence (LS) hazard and rate, and its uncertainty in an area involving two important plains - the Minab and Shamil-Nian plains - in the Hormozgan province, southern Iran. The important features controlling LS hazard were identified by ridge regression. Then, two EDL models were constructed by stacking (SEDL) and voting (VEDL) five dense deep learning (DL) models (model 1 to model 5) for mapping LS hazard. Thereafter, the predictive model performance was assessed by a precision-recall curve and Kolmogorov-Smirnov (KS) plot. A partial dependence plot (PDP), individual conditional expectation plots (ICEP), game theory, and a sensitivity analysis were used for the interpretability of the predictive DL model. According to SEDL - a model with higher accuracy - 34% (1624 km2), 14.7% (698 km2), and 19.2% (912 km2) of the total area were classified as being of very low, low, and moderate hazards, whereas 17.7% (845 km2) and 14.4% (683 km2) of area were classified as being of high and very high hazards, respectively. Based on all interpretability techniques, aquifer loss or groundwater drawdown is the most important feature controlling LS hazard, and it having the greatest impact on the SEDL model output. Based on a Taylor diagram and R2 as model performance assessment indicators, SEDL-AL (with R2 > 95% for training and test datasets) performed better than SEDL for quantify LS rate, the rate of LS ranging between 0 and 48.1 cm. The highest rate of LS occurred in the Minab plain - an area located downstream of the Minab Esteghlal dam. SEDL-AL was used to quantify the uncertainty associated with the LS rate. The observed values fell within predictions provided by SEDL-AL, which indicates a high accuracy of our predictive model. Overall, our newly developed modeling techniques are helpful tools for the spatial mapping of LS susceptibility and rate, and its uncertainty.
摘要：
这一贡献提出了一种基于特征选择的新方法，集成深度学习(EDL)模型，以及用于预测地面沉降(LS)危害和速率的主动学习(AL)方法，以及在霍莫兹甘省涉及两个重要平原的地区-米纳布平原和沙米尔-尼亚平原的不确定性，伊朗南部。通过岭回归确定了控制LS危害的重要特征。然后，通过堆叠（SEDL）和投票（VEDL）五个密集深度学习（DL）模型（模型1至模型5）构建了两个EDL模型，用于映射LS危害。此后,通过精度-召回曲线和Kolmogorov-Smirnov(KS)图评估预测模型性能.部分依赖图(PDP)，个体条件期望图(ICEP)，博弈论,并对预测DL模型的可解释性进行了敏感性分析。根据SEDL-精度更高的模型-34％（1624km2），14.7%(698km2),总面积的19.2%(912平方公里)被归类为非常低，低,和中等危害，而17.7%(845平方公里)和14.4%(683平方公里)的区域被归类为高和非常高的危害，分别。基于所有可解释性技术，含水层损失或地下水下降是控制LS危害的最重要特征，并且它对SEDL模型输出的影响最大。基于泰勒图和R2作为模型绩效评估指标，SEDL-AL（对于训练和测试数据集，R2>95%）在量化LS率方面优于SEDL，LS的速率在0到48.1cm之间。LS的最高发生率发生在Minab平原-位于MinabEsteghlal大坝下游的地区。SEDL-AL用于量化与LS率相关的不确定性。观测值落在SEDL-AL提供的预测范围内，这表明我们的预测模型具有很高的准确性。总的来说,我们新开发的建模技术是LS磁化率和速率空间映射的有用工具，和它的不确定性。
