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首页> 外文期刊>The Science of the Total Environment >Novel ensembles of COPRAS multi-criteria decision-making with logistic regression, boosted regression tree, and random forest for spatial prediction of gully erosion susceptibility
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Novel ensembles of COPRAS multi-criteria decision-making with logistic regression, boosted regression tree, and random forest for spatial prediction of gully erosion susceptibility

机译：具有Logistic回归，增强回归树和随机森林的COPRAS多准则决策新颖合奏，用于空间侵蚀敏感性的空间预测

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摘要

Gully erosion is considered as a severe environmental problem in many areas of the world which causes huge damages to agricultural lands and infrastructures (i.e. roads, buildings, and bridges): however, gully erosion modeling and prediction with high accuracy are still difficult due to the complex interactions of various factors. The objective of this research was to develop and introduce three new ensemble models, which were based on Complex Proportional Assessment of Alternatives (COPRAS), Logistic Regression (LR), Boosted Regression Tree (BRT), Random Forest (RF), and Frequency Ratio (FR) for spatial prediction of gully erosion with a case study at the Najafabad watershed (Iran). For this purpose, a total of 290 head-cut of gullies and 17 conditioning factors were collected and used to establish a geospatial database. Subsequently, FR was used to determine the spatial relationship between the conditioning factors and the head-cut of gullies, whereas RE, BRT, and LR were used to quantify the relative importance of these factors. In the next step, three ensemble gully erosion models, named COPRAS-FR-RF, COPRAS-FR-BRT, and COPRAS-ER-LR were developed and verified. The Success Rate Curve (SRC), and the Prediction Rate Curve (PRC) and their areas under the curves (AUC) were used to check the performance of the three proposed models. The result showed that Soil group, geomorphology, and drainage density factors played the key role on the occurrence of the gully erosion. All the three models have very high degree-of-fit and the prediction performance, the COPRAS-FR-RF model (AUC-SRC = 0.974 and AUC-PRC = 0.929), the COPRAS-FR-BRT model (AUC-SRC = 0.973 and AUC-PRC = 0.928), and the COPRAS-FR-LR model (AUC-SRC = 0.972 and AUC-PRC = 0.926): therefore, it is concluded that they are efficient and new powerful tools which could be used for predicting gully erosion in prone-areas. (C) 2019 Elsevier B.V. All rights reserved.
机译：在世界许多地区，沟壑侵蚀被认为是严重的环境问题，对农田和基础设施（即道路，建筑物和桥梁）造成了巨大破坏：但是，由于沟壑侵蚀的高精度建模和预测仍然很困难。各种因素的复杂相互作用。这项研究的目的是开发和引入三个新的集成模型，这些模型基于备选方案的复杂比例评估（COPRAS），逻辑回归（LR），增强回归树（BRT），随机森林（RF）和频率比（FR），以Najafabad流域（伊朗）为例，对沟壑侵蚀进行空间预测。为此，总共收集了290个沟壑的正面图和17个条件因子，并用于建立地理空间数据库。随后，使用FR来确定条件因素与沟渠的头部之间的空间关系，而使用RE，BRT和LR来量化这些因素的相对重要性。下一步，开发并验证了三个整体沟壑侵蚀模型，分别称为COPRAS-FR-RF，COPRAS-FR-BRT和COPRAS-ER-LR。使用成功率曲线（SRC）和预测率曲线（PRC）及其在曲线下的面积（AUC）来检查所提出的三个模型的性能。结果表明，土壤类型，地貌和排水密度因子在沟壑侵蚀的发生中起关键作用。这三个模型都具有非常高的拟合度和预测性能，COPRAS-FR-RF模型（AUC-SRC = 0.974和AUC-PRC = 0.929），COPRAS-FR-BRT模型（AUC-SRC = 0.973和AUC-PRC = 0.928），以及COPRAS-FR-LR模型（AUC-SRC = 0.972和AUC-PRC = 0.926）：因此，可以得出结论，它们是可用于预测的有效且强大的新工具俯冲区的沟壑侵蚀。 （C）2019 Elsevier B.V.保留所有权利。

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