This study aims to predict land susceptibility (a term that indicates the degree of sensitivity of land to detachment of soil particles by wind) to dust emissions in Yazd province, central Iran, by combining a new integrated data mining (DM) model and the RegCM4 climatic model. The study further determines the relative importance of key factors controlling dust emissions by applying 12 individual DM models. The integrated model is based on the individual models returning Nash Sutcliffe coefficient (NSC) values > 90% for the spatial modelling of land susceptibility to dust emissions and using the area under the curve (AUC) for validation. 13 key factors controlling dust emissions are mapped including soil characteristics, climatic variables, vegetation cover, a Digital Elevation Model (DEM), geology and land use. Based on Spearman clustering analysis and multi-collinearity tests (tolerance coefficient -TC and variance inflation factor -VIF), the effective factors for dust emissions are classified into nine clusters and no multi-collinearity is found among the effective factors. DEM, NDVI (normalized difference vegetation index), geology and calcium carbonate are identified as the most important factors controlling dust emissions. Seven individual models return NSC in the range of 90–98% and are used to generate the integrated model for the final mapping of land susceptibility to dust emissions. Among 851 pixels located in the dust sources, 30% (255 pixels) and 70% (596 pixels) are randomly selected as validation and training datasets, respectively for the new integrated model. Using this model, 9%, 17%, 7% and 67% of the study area correspond to low, moderate, high and very high susceptibility classes, while the validation results in AUC = 99.3%. Simulations with the RegCM4 model reveal high consistency regarding the spatial distribution of the most susceptible areas and dust emissions. Overall, combining DM approaches and physical models is useful in aeolian geomorphology studies.

这项研究旨在通过结合新的综合数据挖掘（DM）模型和RegCM4来预测伊朗中部亚兹德省的土地易感性（该术语表示土地对风吹散的土壤颗粒的敏感性程度）。气候模型。该研究还通过应用12个单独的DM模型确定了控制粉尘排放的关键因素的相对重要性。集成模型基于单个模型，这些模型返回的Nash Sutcliffe系数（NSC）值> 90％，以便对土地对粉尘排放的敏感性进行空间建模，并使用曲线下的面积（AUC）进行验证。确定了控制粉尘排放的13个关键因素，包括土壤特征，气候变量，植被覆盖，数字高程模型（DEM），地质和土地利用。基于Spearman聚类分析和多重共线性测试（容差系数-TC和方差膨胀因子-VIF），将粉尘排放的有效因素分为9个类，在这些有效因素中未发现多重共线性。DEM，NDVI（归一化植被指数），地质和碳酸钙被确定为控制粉尘排放的最重要因素。七个单独的模型返回的NSC在90-98％的范围内，并用于生成集成模型，以最终绘制土地对粉尘排放的敏感性图。在尘埃源中的851个像素中，分别为新的集成模型随机选择了30％（255个像素）和70％（596个像素）作为验证和训练数据集。使用此模型，研究区域的9％，17％，7％和67％对应于低，中，高和非常高的敏感性等级，而验证的结果是AUC = 99.3％。RegCM4模型的仿真显示出最易受影响区域的空间分布和粉尘排放具有高度一致性。总体而言，将DM方法和物理模型结合起来对风沙地貌研究很有用。