必须对土地对风蚀的敏感性进行空间准确的绘制,以减轻其破坏性后果。在这项研究中,第一次,我们开发了一种基于深度学习(DL)和主动学习(AL)模型的新方法,它们的组合(例如,递归神经网络(RNN),RNN-AL,门控经常性单位(GRU),和GRU-AL)和三种解释技术(例如,协同矩阵,Shapley加法扩张(SHAP)决策图,和累积的局部效应(ALE)图),以绘制全球土地对风蚀的敏感性。在这方面,探索了13个变量作为风蚀的控制因素,和其中八个(例如,风速,表层土壤碳含量,表土黏土含量,高程,表土砾石碎片,降水,通过HarrisHawk优化(HHO)特征选择算法选择表土含沙量和土壤水分)作为重要因素。这四个模型用于绘制土地对风蚀的敏感性,并通过三种测量来评估它们的性能,包括接收器工作特征(AUROC)曲线下的面积,累积增益和KolmogorovSmirnov(KS)统计图。结果表明,GRU-AL模型被认为是最准确的,揭示了38.5%,12.6%,10.3%,12.5%和26.1%的全球土地分组非常低,低,中度,对风蚀危险的高和非常高的敏感性等级,分别。应用解释技术来解释八个输入变量对模型输出的贡献和影响。协同图显示,在模型预测中,土壤碳含量与DEM和土壤水分表现出高度协同作用。ALE样图表明,土壤碳含量和降水对土地风蚀敏感性的预测具有负反馈。基于SHAP决策图,土壤水分和DEM对模型产出的贡献最大。结果突出了高纬度地区的新地区(格陵兰南部海岸,阿拉斯加和西伯利亚的热点),表现出很高和非常高的土地对风蚀的敏感性。
Spatial accurate mapping of land susceptibility to wind erosion is necessary to mitigate its destructive consequences. In this research, for the first time, we developed a novel methodology based on deep learning (DL) and active learning (AL) models, their combination (e.g., recurrent neural network (RNN), RNN-AL, gated recurrent units (GRU), and GRU-AL) and three interpretation techniques (e.g., synergy matrix, SHapley Additive exPlanations (SHAP) decision plot, and accumulated local effects (ALE) plot) to map global land susceptibility to wind erosion. In this respect, 13 variables were explored as controlling factors to wind erosion, and eight of them (e.g., wind speed, topsoil carbon content, topsoil clay content, elevation, topsoil gravel fragment, precipitation, topsoil sand content and soil moisture) were selected as important factors via the Harris Hawk Optimization (HHO) feature selection algorithm. The four models were applied to map land susceptibility to wind erosion, and their performance was assessed by three measures consisting of area under of receiver operating characteristic (AUROC) curve, cumulative gain and Kolmogorov Smirnov (KS) statistic plots. The results revealed that GRU-AL model was considered as the most accurate, revealing that 38.5%, 12.6%, 10.3%, 12.5% and 26.1% of the global lands are grouped at very low, low, moderate, high and very high susceptibility classes to wind erosion hazard, respectively. Interpretation techniques were applied to interpret the contribution and impact of the eight input variables on the model\'s output. Synergy plot revealed that the soil carbon content exhibited high synergy with DEM and soil moisture on the model\'s predictions. ALE plot showed that soil carbon content and precipitation had negative feedback on the prediction of land susceptibility to wind erosion. Based on SHAP decision plot, soil moisture and DEM presented the highest contribution on the model\'s output. Results highlighted new regions at high latitudes (southern Greenland coast, hotspots in Alaska and Siberia), which exhibited high and very high land susceptibility to wind erosion.