Spatial mapping of dust sources in arid and semi-arid regions is necessary to mitigate on-site and off-site impacts. In this study, we apply a novel integrated modelling approach including leave one feature out (LOFO) – as a technique for feature selection -, deep learning (DL) models (gcForest and bidirectional long short-term memory (Bi-LSTM)), game theory (GT) and a Gaussian copula-based multivariate (GCBM) model for mapping dust sources in Central Asia (CA). Eight factors (precipitation, cation exchange capacity, bulk density, wind speed, slope, silt content, lithology and coarse fragment content) were selected by LOFO as effective for controlling dust emissions, and were used in the novel modelling process. Six statistical indicators were utilized to assess the performance of the two DL models and a hybrid copula-gcForest model, while a sensitivity analysis of the models was also carried out. The hybrid copula-gcForest model was identified as the most accurate, predicting that 16%, 7.1%, 9.5% and 67.4% of the study area is grouped at low, moderate, high and very high susceptibility classes for dust emissions, respectively. Based on permutation feature importance measure (PFIM) and Shapely Additive exPlanations (SHAP), bulk density, precipitation and coarse fragment content were evaluated as the three most important factors with the highest contributions to the predictive model output. The study area suffers from intense wind erosion and the generated spatial maps of dust sources may be helpful for mitigating and controlling dust phenomena in CA.

干旱和半干旱地区沙尘源的空间测绘对于减轻现场和场外影响是必要的。在这项研究中，我们应用了一种新颖的集成建模方法，包括留一个特征（LOFO）——作为特征选择的技术——、深度学习（DL）模型（gcForest 和双向长短期记忆（Bi-LSTM）），博弈论 (GT) 和基于高斯 copula 的多元 (GCBM) 模型用于绘制中亚 (CA) 的尘埃源。LOFO 选择了八个有效控制粉尘排放的因素（降水量、阳离子交换容量、容重、风速、坡度、淤泥含量、岩性和粗碎屑含量），并将其用于新的建模过程。六个统计指标用于评估两个 DL 模型和混合 copula-gcForest 模型的性能，同时还对模型进行了敏感性分析。混合 copula-gcForest 模型被认为是最准确的，预测研究区域的 16%、7.1%、9.5% 和 67.4% 分别属于低、中、高和非常高的粉尘排放易感性类别。基于置换特征重要性度量（PFIM）和形状相加解释（SHAP），体积密度、沉淀和粗碎片含量被评估为对预测模型输出贡献最大的三个最重要的因素。研究区遭受强烈的风蚀，生成的沙尘源空间图可能有助于减轻和控制加州的沙尘现象。预测研究区域的 16%、7.1%、9.5% 和 67.4% 分别属于低、中、高和非常高的粉尘排放易感性等级。基于置换特征重要性度量（PFIM）和形状相加解释（SHAP），体积密度、沉淀和粗碎片含量被评估为对预测模型输出贡献最大的三个最重要的因素。研究区遭受强烈的风蚀，生成的沙尘源空间图可能有助于减轻和控制加州的沙尘现象。预测研究区域的 16%、7.1%、9.5% 和 67.4% 分别属于低、中、高和非常高的粉尘排放易感性等级。基于置换特征重要性度量（PFIM）和形状相加解释（SHAP），体积密度、沉淀和粗碎片含量被评估为对预测模型输出贡献最大的三个最重要的因素。研究区遭受强烈的风蚀，生成的沙尘源空间图可能有助于减轻和控制加州的沙尘现象。堆积密度、沉淀和粗碎片含量被评估为对预测模型输出贡献最大的三个最重要的因素。研究区遭受强烈的风蚀，生成的沙尘源空间图可能有助于减轻和控制加州的沙尘现象。堆积密度、沉淀和粗碎片含量被评估为对预测模型输出贡献最大的三个最重要的因素。研究区遭受强烈的风蚀，生成的沙尘源空间图可能有助于减轻和控制加州的沙尘现象。