Department of Natural Resources Engineering, University of Hormozgan, Bandar-Abbas, Hormozgan, Iran.
Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran.
Environ Sci Pollut Res Int. 2020 Nov;27(33):42022-42039. doi: 10.1007/s11356-020-10168-6. Epub 2020 Jul 23.
This research developed a more efficient integrated model (IM) based on combining the Nash-Sutcliffe efficiency coefficient (NSEC) and individual data mining (DM) algorithms for the spatial mapping of dust provenance in the Hamoun-e-Hirmand Basin, southeastern Iran. This region experiences severe wind erosion and includes the Sistan plain which is one of the most PM-polluted regions in the world. Due to a prolonged drought over the last two decades, the frequency of dust storms in the study area is increasing remarkably. Herein, 14 factors controlling dust emissions (FCDEs) including soil characteristics, climatic variables, digital elevation map, normalized difference vegetation index, land use and geology were mapped. Correlation and collinearity among the FCDEs were examined by the Pearson test, tolerance coefficient (TC) and variance inflation factor (VIF), with the results suggesting a lack of collinearity between FCDEs. A tree-based genetic algorithm was applied to prioritize and quantify the importance weights of the FCDEs. Thirteen individual data mining models were applied for mapping dust provenance. The model performance was assessed using root mean square error, mean absolute error and NSEC. Based on clustering analysis, the 13 DM models were grouped into five clusters and then the cluster with the highest NSEC values used in an integrated modelling process. Based on the results, the IM (NSEC = 93%) outperformed the individual DM models (the NSEC values range between 51 and 92%). Using the IM, 11, 5, 7 and 77% of the total study area were classified into low, moderate, high and very high susceptibility classes for dust provenance, respectively. Overall, the results illustrate the benefits of an IM for mapping spatial variation in the susceptibility of catchment areas to act as dust sources.
本研究开发了一种更有效的综合模型（IM），该模型基于纳什-萨克特效率系数（NSEC）和个体数据挖掘（DM）算法相结合，用于伊朗东南部哈姆恩-赫尔曼德盆地尘埃来源的空间制图。该地区经历严重的风蚀，包括锡斯坦平原，这是世界上污染最严重的 PM 地区之一。由于过去二十年来持续干旱，研究区域的沙尘暴频率显著增加。在此，绘制了 14 个控制尘埃排放的因素（FCDE），包括土壤特性、气候变量、数字高程图、归一化差异植被指数、土地利用和地质。通过 Pearson 检验、容忍系数（TC）和方差膨胀因子（VIF）检查了 FCDE 之间的相关性和共线性，结果表明 FCDE 之间不存在共线性。应用基于树的遗传算法对 FCDE 进行优先级排序和量化重要性权重。应用了 13 个个体数据挖掘模型进行尘埃来源的制图。使用均方根误差、平均绝对误差和 NSEC 评估模型性能。基于聚类分析，将 13 个 DM 模型分为五个簇，然后使用聚类中 NSEC 值最高的模型进行综合建模过程。基于结果，IM（NSEC = 93%）优于个体 DM 模型（NSEC 值范围在 51 到 92%之间）。使用 IM，将总研究区域的 11%、5%、7%和 77%分别归类为尘埃来源易感性的低、中、高和极高敏感性类别。总体而言，结果表明 IM 可用于制图集水区对尘埃源的敏感性的空间变化。
