当前位置: X-MOL 学术Water › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Susceptibility to Gully Erosion: Applying Random Forest (RF) and Frequency Ratio (FR) Approaches to a Small Catchment in Ethiopia
Water ( IF 3.0 ) Pub Date : 2021-01-18 , DOI: 10.3390/w13020216
Selamawit Amare , Eddy Langendoen , Saskia Keesstra , Martine van der Ploeg , Habtamu Gelagay , Hanibal Lemma , Sjoerd E. A. T. M. van der Zee

Soil erosion by gullies in Ethiopia is causing environmental and socioeconomic problems. A sound soil and water management plan requires accurately predicted gully erosion hotspot areas. Hence, this study develops a gully erosion susceptibility map (GESM) using frequency ratio (FR) and random forest (RF) algorithms. A total of 56 gullies were surveyed, and their extents were derived by digitizing Google Earth imagery. Literature review and a multicollinearity test resulted in 14 environmental variables for the final analysis. Model prediction potential was evaluated using the area under the curve (AUC) method. Results showed that the best prediction accuracy using the FR and RF models was obtained by using the top four most important gully predictor factors: drainage density, elevation, land use, and groundwater table. The notion that the groundwater table is one of the most important gully predictor factors in Ethiopia is a novel and significant quantifiable finding and is critical to the design of effective watershed management plans. Results from separate variable importance analyses showed land cover for Nitisols and drainage density for Vertisols as leading factors determining gully locations. Factors such as texture, stream power index, convergence index, slope length, and plan and profile curvatures were found to have little significance for gully formation in the studied catchment.

中文翻译:

沟壑侵蚀的易感性:在埃塞俄比亚的小流域采用随机森林(RF)和频率比(FR)方法

埃塞俄比亚沟壑造成的土壤侵蚀正在引起环境和社会经济问题。完善的水土管理计划需要准确预测沟壑侵蚀的热点地区。因此,本研究使用频率比(FR)和随机森林(RF)算法开发了一条沟壑侵蚀敏感性图(GESM)。总共对56个沟壑进行了调查,其范围是通过数字化Google Earth图像得出的。文献综述和多重共线性测试得出了14个环境变量,用于最终分析。使用曲线下面积(AUC)方法评估模型预测潜力。结果表明,使用FR和RF模型获得的最佳预测精度是通过使用最重要的四个沟壑预测因子:排水密度,海拔,土地利用和地下水位来获得的。地下水位是埃塞俄比亚最重要的沟壑预报因子之一的观点是一个新颖且重要的可量化发现,对设计有效的流域管理计划至关重要。各个变量重要性分析的结果表明,尼替洛尔的土地覆盖率和Vertisols的排水密度是决定沟壑位置的主要因素。发现诸如质地,河流功率指数,收敛指数,坡长,平面和剖面曲率等因素对所研究流域的沟渠形成影响不大。各个变量重要性分析的结果表明,尼替洛尔的土地覆盖率和Vertisols的排水密度是决定沟壑位置的主要因素。研究中的流域的质地,溪流功率指数,会聚指数,坡长,平面曲率和剖面曲率等因素对沟壑形成的意义不大。各个变量重要性分析的结果表明,尼替洛尔的土地覆盖率和Vertisols的排水密度是决定沟壑位置的主要因素。研究中的流域的质地,溪流功率指数,会聚指数,坡长,平面曲率和剖面曲率等因素对沟壑形成的意义不大。
更新日期:2021-01-18
down
wechat
bug