Abstract

Spatial modelling of gully erosion at regional level is very relevant for local authorities to establish successful counter-measures and to change land-use planning. This work is exploring and researching the potential of a genetic algorithm-extreme gradient boosting (GE-XGBoost) hybrid computer education solution for spatial mapping of the susceptibility of gully erosion. The new machine learning approach is to combine the extreme gradient boosting machine (XGBoost) and the genetic algorithm (GA). The GA metaheuristic is being used to improve the efficiency of the XGBoost classification approach. A GIS database has been developed that contains recorded instances of gully erosion incidents and 18 conditioning variables. These parameters are used as predictive variables used to assess the condition of non-erosion or erosion in a given region within the Kohpayeh-Sagzi River Watershed research area in Iran. Exploratory results indicate that the proposed GE-XGBoost model is superior to the other benchmark solution with the desired predictive precision (89.56%). Therefore, the newly built model may be a promising method for large-scale mapping of gully erosion susceptibility.

摘要

区域级沟壑侵蚀的空间模型与地方当局建立成功的对策和改变土地利用规划非常相关。这项工作正在探索和研究遗传算法-极端梯度增强（GE-XGBoost）混合计算机教育解决方案在沟壑侵蚀敏感性空间分布方面的潜力。新的机器学习方法是将极限梯度提升机（XGBoost）和遗传算法（GA）相结合。GA元启发式算法可用于提高XGBoost分类方法的效率。已经开发了一个GIS数据库，其中包含记录的沟壑侵蚀事件实例和18个条件变量。这些参数用作预测变量，用于评估伊朗Kohpayeh-Sagzi河流域研究区域内给定区域的非侵蚀或侵蚀状况。探索性结果表明，提出的GE-XGBoost模型以所需的预测精度（89.56％）优于其他基准解决方案。因此，新建立的模型可能是一种有前途的大规模侵蚀性沟壑制图方法。