.png){kind=icon}
返回
Nonnegative tensor factorization for contaminant source identification
.png){kind=icon}
OA
AI
DOI：10.1016/j.jconhyd.2018.11.010.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Unsupervised Machine Learning (ML) is becoming increasingly popular for solving various types of data analytics problems including feature extraction, blind source separation, exploratory analyses, model diagnostics, etc. Here, we have developed a new unsupervised ML method based on Nonnegative Tensor Factorization (NTF) for identification of the original groundwater types (including contaminant sources) present in geochemical mixtures observed in an aquifer. Frequently, groundwater types with different geochemical signatures are related to different background and/or contamination sources. The characterization of groundwater mixing processes is a challenging but very important task critical for any environmental management project aiming to characterize the fate and transport of contaminants in the subsurface and perform contaminant remediation. This task typically requires solving complex inverse models representing groundwater flow and geochemical transport in the aquifer, where the inverse analysis accounts for available site data. Usually, the model is calibrated against the available data characterizing the spatial and temporal distribution of the observed geochemical types. Numerous different geochemical constituents and processes may need to be simulated in these models which further complicates the analyses. Additionally, the application of inverse methods may introduce biases in the analyses through the assumptions made in the model development process. Here, we substitute the model inversion with unsupervised ML analysis. The ML analysis does not make any assumptions about underlying physical and geochemical processes occurring in the aquifer. Our ML methodology, called NTFk, is capable of identifying (1) the unknown number of groundwater types (contaminant sources) present in the aquifer, (2) the original geochemical concentrations (signatures) of these groundwater types and (3) spatial and temporal dynamics in the mixing of these groundwater types. These results are obtained only from the measured geochemical data without any additional site information. In general, the NTFk methodology allows for interpretation of large high-dimensional datasets representing diverse spatial and temporal components such as state variables and velocities. NTFk has been tested on synthetic and real-world site three-dimensional datasets. The NTFk algorithm is designed to work with geochemical data represented in the form of concentrations, ratios (of two constituents; for example, isotope ratios), and delta notations (standard normalized stable isotope ratios).
Keyword:
Nonnegative tensor factorization
Tucker decomposition
Feature Extraction
Exploratory analysis
Blind Source Separation
Robustness analysis
Unsupervised machine learning
Groundwater contamination
Source identification
Advection-diffusion transport
Geochemical signatures
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.4
论文数:
3.5K
被引数:
6.5K
机构
引用论文
分享收藏Temporal evolution of groundwater composition in an alluvial aquifer (Pisuerga River, Spain) by principal component analysis.png)
WATER RESEARCH
WATER RESEARCHIF12.4
PREAI
分享收藏M Pathway and Areas 44 and 45 Are Involved in Stereoscopic Recognition Based on Binocular Disparity.
OAAI
OAAI分享收藏分享收藏Heavy metal tolerance of marine phytoplankton. IV. Combined effect of zinc and cadmium on growth and uptake in some marine diatoms海洋浮游植物对重金属的耐受性。四。锌和镉对某些海洋硅藻生长和吸收的综合影响
PREAI
分享收藏
查看更多内容