Abstract：
Land subsidence is a worldwide geo-environmental hazard. Clarifying the influencing factors of land subsidence hazards susceptibility (LSHS) and their spatial distribution are critical to the prevention and control of subsidence disasters. In this study, we selected natural and anthropogenic features or variables on LSHS and used the interpretable convolutional neural network (CNN) method to successfully construct a LSHS model in China. The model performed well, with AUC and F1-score testing set accuracies reaching 0.9939 and 0.9566, respectively. The interpretable method of SHapley Additive exPlanations (SHAP) was use to elucidate the individual contribution of input features to the predictions of CNN model. The importance ranking of model variables showed that population, gross domestic product (GDP) and groundwater storage (GWS) change are the three major factors that affect China\'s land subsidence. During year 2004-2016, an area of 237.6 thousand km2 was classified as high and very high LSHS, mainly concentrated in the North China Plain, central Shanxi, southern Shaanxi, Shanghai and the junction of Jiangsu and Zhejiang. There will be 333.82-343.12 thousand km2 of areas located in the high and very high LSHS in the mid-21st century (2030-2059) and 361.9-385.92 thousand km2 of areas in the late-21st century (2070-2099). Future population exposure to high and very high LSHS will be 252.12-270.19 million people (mid-21st century) and 196.14-274.50 million people (late-21st century), respectively, compared with the historical exposure of 210.99 million people. The proportion of future railway and road exposure will reach 14.63 %-14.89 % and 11.51 %-11.82 % in the mid-21st century, and 15.46 %-17.12 % and 12.35 %-13.11 % in the late-21st century, respectively. Our findings provide an important information for creating regional adaptation policies and strategies to mitigate damage induced by subsidence.
摘要：
地面沉降是一种世界性的地质环境危害。明确地面沉降灾害易发性(LSHS)的影响因素及其空间分布对地面沉降灾害的防治至关重要。在这项研究中,我们在LSHS上选择了自然和人为特征或变量，并使用可解释的卷积神经网络（CNN）方法在中国成功构建了LSHS模型。该模型表现良好，AUC和F1分数测试集的准确性分别达到0.9939和0.9566。Shapley加法扩张（SHAP）的可解释方法用于阐明输入特征对CNN模型预测的个人贡献。模型变量的重要性排序表明，人口，国内生产总值(GDP)和地下水储量(GWS)变化是影响我国地面沉降的三大因素。在2004-2016年期间，237.6万平方公里的面积被归类为高和非常高的LSHS，主要集中在华北平原,山西中部,陕南,上海与江浙交界处。在21世纪中叶（2030-2059），将有333.82-343.12万平方公里的区域位于高和非常高的LSHS中，在21世纪后期（2070-2099）将有361.9-385.92万平方公里的区域。未来人口暴露于高和非常高的LSHS将是252.12-270.19万人（21世纪中叶）和196.14-274.50万人（21世纪后期），分别,与历史曝光的21099万人相比。未来铁路和公路暴露比例在21世纪中叶将达到14.63%-14.89%和11.51%-11.82%，在21世纪后期，分别为15.46%-17.12%和12.35%-13.11%，分别。我们的发现为制定区域适应政策和策略以减轻沉陷造成的损害提供了重要信息。
