摘要
由一名新闻记者-机器人与机器学习日报的工作人员新闻编辑最新关于机器学习的研究成果已经发表。根据消息来源来自印度加尔各答的NewsRx Corre Spondents的研究表明，“来自斜坡上游的水文连通性”向河谷底坡和主河道的下坡，引发了沟谷的形成和土地退化继续发生场外侵蚀，被认为是潜在沉积物剥离的最有效驱动因素。本研究试图确定沟蚀敏感性(GES)与水文亚热带湿润河流域康萨巴提(KRB)的Conne Citival Pathway四马网络学习算法(MLALs)，如随机森林(RF)，支持向量Mac Hine(SVM)，极端梯度Boosting(XGB)、人工神经网络(ANN)和连通指数(IC)水文连通性绘图。 ”
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews – Current study results on Machine Learning have be en published. According to news originatingfrom Kolkata, India, by NewsRx corre spondents, research stated, “Hydrological connectivity from upslopeto downslope of valley floor and main channel, triggered the gully initiation and associated land degradationcontinue occurring off-site erosion as considered most effecti ve drivers on potential sediment detachment.Present study attempted to identify the linkage between gullies erosion susceptibility (GES) and hydrologicalconne ctivity pathway in sub-tropical humid river basin Kangsabati (KRB) using four ma chine-learningalgorithms (MLALs) such as Random Forest (RF), Support Vector Mac hine (SVM), Extreme GradientBoosting (XGB), Artificial Neural Network (ANN) for GES mapping, and connectivity index (IC) forhydrological connectivity mapping. ”
NETL
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