Abstract

Piping erosion is one of the water erosions that cause significant changes in the landscape, leading to environmental degradation. To prevent losses resulting from tube growth and enable sustainable development, developing high-precision predictive algorithms for piping erosion is essential. Boosting is a classic algorithm that has been successfully applied to diverse computer vision tasks. Therefore, this work investigated the predictive performance of the Boosted Linear Model (BLM), Boosted Regression Tree (BRT), Boosted Generalized Linear Model (Boost GLM), and Deep Boosting models for piping erosion susceptibility mapping in Zarandieh Watershed located in the Markazi province of Iran. A piping inventory map including 152 piping erosion locations was prepared for algorithm training and testing. 18 initial predisposing factors (altitude, slope, plan curvature, profile curvature, distance from river, drainage density, distance from road, rainfall, land use, soil type, bulk density, CEC, pH, clay, silt, sand, topographical position index (TPI), topographic wetness index (TWI)) was derived from multiple remote sensing (RS) sources to determine the piping erosion prone areas. The most significant predisposing factors were selected using multi-collinearity analysis which indicates linear correlations between predisposing factors. Finally, the results were evaluated for Sensitivity, Specificity, Positive predictive values (PPV) and Negative predictive value (NPV), and Receiver Operation characteristic (ROC) curve. The best Sensitivity (0.80), Specificity (0.84), PPV (0.85), NPV (0.79), and ROC (0.93), were obtained by Deep Boosting model. The results of the piping erosion susceptibility study in agricultural land use showed that 41% of agricultural lands are very sensitive to piping erosion. This outcome will enable natural resource managers and local planners to assess and take effective decisions to minimize damages to agricultural land use by accurately identifying the most vulnerable areas. Hence, this research proved Deep Boosting model’s ability for piping erosion susceptibility mapping in comparison to other popular methods such as BLM, BRT, and Boost GLM.

摘要

管道侵蚀是导致景观发生重大变化、导致环境退化的水侵蚀之一。为了防止管道生长造成的损失并实现可持续发展，开发高精度的管道腐蚀预测算法至关重要。Boosting 是一种经典算法，已成功应用于各种计算机视觉任务。因此，这项工作研究了提升线性模型 (BLM)、提升回归树 (BRT)、提升广义线性模型 (Boost GLM) 和深度提升模型在位于 Markazi 省的 Zarandieh 流域的管道侵蚀敏感性映射的预测性能伊朗的。为算法训练和测试准备了包括 152 个管道侵蚀位置的管道库存图。18 个初始诱发因素（海拔、坡度、平面曲率、剖面曲率、与河流的距离、排水密度、与道路的距离、降雨量、土地利用、土壤类型、容重、CEC、pH、粘土、淤泥、沙子、地形位置指数 (TPI)、地形湿度指数 (TWI )) 来自多个遥感 (RS) 源，以确定管道腐蚀易发区域。使用多重共线性分析选择最显着的易感因素，该分析表明易感因素之间存在线性相关性。最后，评估结果的敏感性、特异性、阳性预测值（PPV）和阴性预测值（NPV），以及接受者操作特征（ROC）曲线。通过 Deep Boosting 模型获得最佳灵敏度 (0.80)、特异性 (0.84)、PPV (0.85)、NPV (0.79) 和 ROC (0.93)。农业用地管道侵蚀敏感性研究结果表明，41%的农业用地对管道侵蚀非常敏感。这一成果将使自然资源管理者和地方规划者能够评估并做出有效决策，通过准确识别最脆弱的地区来最大限度地减少对农业土地使用的损害。因此，与 BLM、BRT 和 Boost GLM 等其他流行方法相比，本研究证明了 Deep Boosting 模型在管道侵蚀敏感性映射方面的能力。