摘要
机器人与机器学习每日新闻的一位新闻记者兼新闻编辑-机器学习的最新研究结果已经发表。根据NewsRx记者从Oum El Bouaghi发来的消息，研究表明，“缺乏土壤侵蚀数据和其他流域信息继续限制土壤侵蚀建模。为了克服这些局限性，许多研究人员转向机器学习模型来分析和建模复杂的水侵蚀过程，并将其与经验模型结合起来。”我们的新闻记者从Larbi Ben MHidi大学的研究中获得了一句话：“Beni Haroun大坝流域由于特殊的地理环境和土地做法而面临水土流失，对农业和自然资源开发构成严重威胁。因此，本研究试图利用GIS环境中的5个关键因素(降雨侵蚀力、土壤侵蚀力、地形、覆盖管理和保护实践因素),利用修正的通用土壤侵蚀方程(RUSLE)识别土壤侵蚀敏感区,并将5个RUSLE参数和模型输出集成到两个机器学习(ML)算法中。结果表明,西北地区土壤侵蚀最易发生,东南地区土壤侵蚀最易发生,西北地区土壤侵蚀最易发生,而东南地区土壤侵蚀最易发生,西北地区土壤侵蚀最易发生。RUSLE模型和基于RT模型的RUSLE模型在侵蚀严重程度分类方面取得了几乎相同的结果，估计年平均土壤侵蚀量分别为17.5和17.69(t HA-1y-1)，而基于R andom森林RF的RUSLE模型的结果略有不同，分别为23.89(t HA-1y-1)。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Current study results on Machine Learn ing have been published. According to news originating from Oum El Bouaghi, Alge ria, by NewsRx correspondents, research stated, “The lack of soil erosion data a nd other information about watersheds continues to limit soil erosion modeling. To overcome these limitations, many researchers have turned to machine learning models to analyze and model the complex water erosion processes and integrate th em with empirical models.” Our news journalists obtained a quote from the research from the University of L arbi Ben MHidi, “The Beni Haroun dam watershed faces soil erosion due to specifi c geo-environmental settings and land practices. It poses serious threats to agr icultural and natural resource development. For these reasons, this study attemp ts to identify soil erosion susceptible zones using the Revised Universal Soil L oss Equation (RUSLE) using five key factors (rainfall erosivity, soil erodibilit y, topography, cover management and conservation practice factor) in GIS environ ment. Furthermore, we integrated the five RUSLE parameters and the model outputs into two machine learning (ML) algorithms, namely Random Forest (RF) and Random Tree (RT). The proposed models underwent training on 70% of the d ataset and were subsequently validated on the remaining 30%. Our re sults indicated that the most vulnerable to severe soil erosion was concentrated in northwest regions, in contrast to the southeastern regions, which most occup y low erosion and moderate erosion. RUSLE and RT-based RUSLE models yielded near ly identical results in classifying erosion severity,estimating the annual aver age soil erosion at 17.5 and 17.69 (t ha-1y-1), respectively. In contrast, the R andom Forest RF-based RUSLE model presented slightly divergent findings 23.89 (t ha-1y-1).”
NETL
NSTL