Assessment of the importance of gully erosion effective factors using Boruta algorithm and its spatial modeling and mapping using three machine learning algorithms
Assessment of the importance of gully erosion effective factors using Boruta algorithm and its spatial modeling and mapping using three machine learning algorithms
复制标题
DOI：
10.1016/j.geoderma.2018.12.042
复制
发表时间：
2019-04-15
期刊：
影响因子：
6.1
通讯作者：
Afzali, Sayed Fakhreddin
中科院分区：
文献类型：
--
作者：
Amiri, Mandis;Pourghasemi, Hamid Reza;Afzali, Sayed Fakhreddin
由于该地区特定的地形气候条件和人为活动，马哈洛分水岭最近出现了许多沟壑。本研究旨在解决这一问题，通过三种机器学习算法(包括RF、支持向量机和BRT)生成伊朗法尔斯省马哈洛分水岭的沟道侵蚀预测图。此外，本研究还尝试使用Boruta算法来考虑影响因素在沟谷侵蚀发生中的重要性。为此，通过广泛的实地调查以及使用已经编制的马哈洛流域沟渠栅格地图，确定了沟壑侵蚀的地点。在此基础上，确定了高程、坡度、坡向、平面曲率、TWI、距河流距离、距道路距离、排水密度、岩性、年平均降雨量、NDVI、土地利用和土壤特性(pH、粘粒含量、电导率EC和粉粒含量)等16个引起沟道侵蚀的因素，并在地理信息系统中对它们进行了分类。利用证据信任函数(EBF)算法定义了各因子与沟道侵蚀的关系，并确定了各因子类别的权重。另一方面，各因素间的共线性检验结果表明，含砂剂的VIF>；5，因此从模型中剔除了这个协变量。用Boruta算法对影响因素重要性的计算结果表明，土地利用、离河距离和粘粒含量三个因素对研究区沟道侵蚀发生的影响最为显著。最后，利用R统计软件中的RF模型、BRT模型和支持向量机模型生成了沟谷侵蚀敏感性图。在建模过程中使用30%的未使用位置以及接收器操作特征(ROC)曲线来评估机器学习技术的结果。此外，在目前的研究中，尝试使用敏感率、特异率、Cohen‘s Kappa和4重交叉验证措施来评估模型的拟合性能和它们的稳健性。结果表明，最终的沟谷侵蚀敏感度图与情景7和情景9验证数据集的AUC值具有较高的精度(支持向量机分别为0.957，0.975，Rf=0.991，0.986，BRT=0.913，0.913)。拟合性能度量和稳健性技术(4次交叉验证)也证实了所获得的验证结果。为了控制和防止马哈洛流域的这类侵蚀，应在初级阶段，特别是在沟壑侵蚀开始时，采取保护行动和流域管理措施，以控制沟壑侵蚀的发展。
The Maharloo watershed has witnessed many gullies in the recent due to the specific topo-climatic conditions and man-made activities in that area. The present study is set out to address this issue by producing gully erosion prediction maps via three machine learning algorithms including RF, SVM and BRT in Maharloo watershed, Fars province, Iran. Also, this research attempted to consider the importance of effective factors in the occurrence of gully erosion using Boruta algorithm. To this end, gully erosion locations were identified by extensive field surveys as well as the use of already prepared gully raster map of Maharloo watershed. Then, sixteen causative factors of gully erosion such as elevation, slope degree, slope aspect, plan curvature, TWI, distance from rivers, distance from roads, drainage density, lithology, annual mean rainfall, NDVI, land use and some soil characteristics (pH, clay percent, electrical conductivity-EC, and silt percent) were identified and their maps were produced and classified in the GIS. In this study, the relationships among each agent and gully erosion were defined employing the evidential belief function (EBF) algorithm and the weight of each factor's classes was determined. On the other hand, the results of the collinearity test among the factors showed that sand percentage agent had a VIF > 5; therefore, this covariate was removed from the model. Also, the results of the importance of effective factors using Boruta algorithm indicated that three factors including land use, distance from river, and clay percent had the most noticeable importance in the occurrence of gully erosion in the study area. Finally, the gully erosion susceptibility maps were produced using the RF, BRT, and SVM models in the R statistical software. The results of machine learning techniques were evaluated employing 30% of unused locations in the modeling process as well as the receiver operating characteristic (ROC) curve. Also, in the current research, try to assess the fitting performance of models and their robustness using sensitivity rate, specificity rate, Cohen's Kappa, and 4-fold cross-validation measures. Results showed that the final gully erosion susceptibility maps had an excellent accuracy with AUC values of validation data sets by scenarios 7 and 9 independent factors on gully erosion, respectively (SVM = 0.957, 0.975, RF = 0.991, 0.986, BRT = 0.913, 0.913). The fitting performance measures and robustness technique (4-fold cross-validation) also confirmed the achieved validation results. In order to control and prevent this type of erosion in the Maharloo watershed, there should be protective actions and watershed management measures in place at the primary stages, especially at the beginning of the gully erosion, to control the development of the gully erosion.