利用快速多输出相关性矢量回归建立非同质土壤的垂直和水平透水速度模型

IF 2.6 4区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Big Data Pub Date : 2024-08-01 Epub Date: 2023-03-14 DOI:10.1089/big.2022.0125
Babak Vaheddoost, Shervin Rahimzadeh Arashloo, Mir Jafar Sadegh Safari
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引用次数: 0

摘要

本研究通过快速多输出相关性向量回归(FMRVR)来联合确定水在多孔介质中的水平和垂直运动。为此,使用了在有机玻璃制成的尺寸为 300 × 300 × 150 毫米的沙箱中进行的实验数据集。使用粒度为 0.5-1 毫米的随机混合物来模拟多孔介质。在实验中,使用了 2、3、7 和 12 厘米的箱壁,以及不同的注入位置,即从上游截壁测量的 130.7、91.3 和 51.8 毫米。然后,将示踪剂的笛卡尔坐标、时间间隔、每个设置中的壁长以及两个用于确定初始点的虚拟变量作为自变量,共同估算多孔介质中水的水平和垂直运动速度。此外,还使用了多线性回归、随机森林和支持向量回归等方法来交替使用 FMRVR 方法得出的结果。得出的结论是,FMRVR 的效果优于其他模型,但水平渗透估算的不确定性大于垂直渗透估算。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Vertical and Horizontal Water Penetration Velocity Modeling in Nonhomogenous Soil Using Fast Multi-Output Relevance Vector Regression.

A joint determination of horizontal and vertical movement of water through porous medium is addressed in this study through fast multi-output relevance vector regression (FMRVR). To do this, an experimental data set conducted in a sand box with 300 × 300 × 150 mm dimensions made of Plexiglas is used. A random mixture of sand having size of 0.5-1 mm is used to simulate the porous medium. Within the experiments, 2, 3, 7, and 12 cm walls are used together with different injection locations as 130.7, 91.3, and 51.8 mm measured from the cutoff wall at the upstream. Then, the Cartesian coordinated of the tracer, time interval, length of the wall in each setup, and two dummy variables for determination of the initial point are considered as independent variables for joint estimation of horizontal and vertical velocity of water movement in the porous medium. Alternatively, the multi-linear regression, random forest, and the support vector regression approaches are used to alternate the results obtained by the FMRVR method. It was concluded that the FMRVR outperforms the other models, while the uncertainty in estimation of horizontal penetration is larger than the vertical one.

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来源期刊
Big Data
Big Data COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
9.10
自引率
2.20%
发文量
60
期刊介绍: Big Data is the leading peer-reviewed journal covering the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data. The Journal addresses questions surrounding this powerful and growing field of data science and facilitates the efforts of researchers, business managers, analysts, developers, data scientists, physicists, statisticians, infrastructure developers, academics, and policymakers to improve operations, profitability, and communications within their businesses and institutions. Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address current challenges and enforce effective efforts to organize, store, disseminate, protect, manipulate, and, most importantly, find the most effective strategies to make this incredible amount of information work to benefit society, industry, academia, and government. Big Data coverage includes: Big data industry standards, New technologies being developed specifically for big data, Data acquisition, cleaning, distribution, and best practices, Data protection, privacy, and policy, Business interests from research to product, The changing role of business intelligence, Visualization and design principles of big data infrastructures, Physical interfaces and robotics, Social networking advantages for Facebook, Twitter, Amazon, Google, etc, Opportunities around big data and how companies can harness it to their advantage.
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